{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "26cf1c5e",
   "metadata": {
    "papermill": {
     "duration": 0.008781,
     "end_time": "2025-09-14T10:18:13.451590",
     "exception": false,
     "start_time": "2025-09-14T10:18:13.442809",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 0. 环境与数据 / Environment and Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d107d4a",
   "metadata": {
    "papermill": {
     "duration": 0.007262,
     "end_time": "2025-09-14T10:18:13.467014",
     "exception": false,
     "start_time": "2025-09-14T10:18:13.459752",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 0.1 说明 / Description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a686444c",
   "metadata": {
    "papermill": {
     "duration": 0.007184,
     "end_time": "2025-09-14T10:18:13.481759",
     "exception": false,
     "start_time": "2025-09-14T10:18:13.474575",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "本 Notebook 在 Kaggle 平台运行。\n",
    "- Notebook 链接：[https://www.kaggle.com/code/yiquanxiao/cosmetics-e-commerce-customer-segmentation-more](https://www.kaggle.com/code/yiquanxiao/cosmetics-e-commerce-customer-segmentation-more)\n",
    "- 前作（EDA+异动分析）：[https://www.kaggle.com/code/yiquanxiao/cosmetics-e-commerce-eda-anomaly-analysis](https://www.kaggle.com/code/yiquanxiao/cosmetics-e-commerce-eda-anomaly-analysis)\n",
    "\n",
    "---\n",
    "\n",
    "This notebook runs on the Kaggle platform.\n",
    "- Notebook Link: [https://www.kaggle.com/code/yiquanxiao/cosmetics-e-commerce-customer-segmentation-more](https://www.kaggle.com/code/yiquanxiao/cosmetics-e-commerce-customer-segmentation-more)\n",
    "- Previous Work (EDA + Anomaly Detection): [https://www.kaggle.com/code/yiquanxiao/cosmetics-e-commerce-eda-anomaly-analysis](https://www.kaggle.com/code/yiquanxiao/cosmetics-e-commerce-eda-anomaly-analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfcfdf57",
   "metadata": {
    "papermill": {
     "duration": 0.007308,
     "end_time": "2025-09-14T10:18:13.497361",
     "exception": false,
     "start_time": "2025-09-14T10:18:13.490053",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 0.2 依赖 / Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cdf22b83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:18:13.513767Z",
     "iopub.status.busy": "2025-09-14T10:18:13.513408Z",
     "iopub.status.idle": "2025-09-14T10:18:18.098362Z",
     "shell.execute_reply": "2025-09-14T10:18:18.097214Z"
    },
    "papermill": {
     "duration": 4.595393,
     "end_time": "2025-09-14T10:18:18.100368",
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     "start_time": "2025-09-14T10:18:13.504975",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "from pathlib import Path\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab64d1dc",
   "metadata": {
    "papermill": {
     "duration": 0.007556,
     "end_time": "2025-09-14T10:18:18.116784",
     "exception": false,
     "start_time": "2025-09-14T10:18:18.109228",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1. 数据准备 / Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b245cd7e",
   "metadata": {
    "papermill": {
     "duration": 0.008041,
     "end_time": "2025-09-14T10:18:18.133000",
     "exception": false,
     "start_time": "2025-09-14T10:18:18.124959",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 1.1 读取与合并 / Load & Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "469d4299",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:18:18.150001Z",
     "iopub.status.busy": "2025-09-14T10:18:18.149607Z",
     "iopub.status.idle": "2025-09-14T10:25:24.264686Z",
     "shell.execute_reply": "2025-09-14T10:25:24.263666Z"
    },
    "papermill": {
     "duration": 426.138887,
     "end_time": "2025-09-14T10:25:24.279814",
     "exception": false,
     "start_time": "2025-09-14T10:18:18.140927",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "合并后数据量 / Total rows after concat: 20,692,840\n"
     ]
    }
   ],
   "source": [
    "# 1.1.1 批量读取 / Batch Load\n",
    "# 指定数据目录（根据实际路径调整）/ Adjust the path if needed\n",
    "data_path = Path(\"/kaggle/input/ecommerce-events-history-in-cosmetics-shop\")\n",
    "\n",
    "# 匹配 5 个 CSV 文件 / Match the five CSV files\n",
    "csv_files = {\n",
    "    \"2019-10\": \"2019-Oct.csv\", \n",
    "    \"2019-11\": \"2019-Nov.csv\", \n",
    "    \"2019-12\": \"2019-Dec.csv\", \n",
    "    \"2020-01\": \"2020-Jan.csv\", \n",
    "    \"2020-02\": \"2020-Feb.csv\"\n",
    "}\n",
    "files_path = {}\n",
    "for key, name in csv_files.items():\n",
    "    files_path[key] = data_path / name\n",
    "\n",
    "\n",
    "# 统一列类型 / Standardize dtypes before reading\n",
    "dtype_map = {\n",
    "    \"event_type\": \"category\",\n",
    "    \"product_id\": \"int64\",\n",
    "    \"category_id\": \"int64\",\n",
    "    \"category_code\": \"string\",\n",
    "    \"brand\": \"string\",\n",
    "    \"price\": \"float64\",\n",
    "    \"user_id\": \"int64\", \n",
    "}\n",
    "\n",
    "dfs = []\n",
    "for key, fp in files_path.items():\n",
    "    df = pd.read_csv(\n",
    "        fp,\n",
    "        dtype=dtype_map,\n",
    "        parse_dates=[\"event_time\"]   # 直接解析时间列 / parse datetime column\n",
    "    )\n",
    "    df[\"year_month\"] = key      # 标注来源文件 / track source file\n",
    "    dfs.append(df)\n",
    "\n",
    "\n",
    "# 1.1.2 纵向合并 / Vertical Concatenation\n",
    "df_raw = pd.concat(dfs, ignore_index=True)\n",
    "print(f\"合并后数据量 / Total rows after concat: {df_raw.shape[0]:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f9f3b346",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:25:24.298060Z",
     "iopub.status.busy": "2025-09-14T10:25:24.297766Z",
     "iopub.status.idle": "2025-09-14T10:25:24.319343Z",
     "shell.execute_reply": "2025-09-14T10:25:24.318300Z"
    },
    "papermill": {
     "duration": 0.032537,
     "end_time": "2025-09-14T10:25:24.320877",
     "exception": false,
     "start_time": "2025-09-14T10:25:24.288340",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20692840 entries, 0 to 20692839\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Dtype              \n",
      "---  ------         -----              \n",
      " 0   event_time     datetime64[ns, UTC]\n",
      " 1   event_type     category           \n",
      " 2   product_id     int64              \n",
      " 3   category_id    int64              \n",
      " 4   category_code  string             \n",
      " 5   brand          string             \n",
      " 6   price          float64            \n",
      " 7   user_id        int64              \n",
      " 8   user_session   object             \n",
      " 9   year_month     object             \n",
      "dtypes: category(1), datetime64[ns, UTC](1), float64(1), int64(3), object(2), string(2)\n",
      "memory usage: 1.4+ GB\n"
     ]
    }
   ],
   "source": [
    "df_raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f0cc442",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:25:24.338435Z",
     "iopub.status.busy": "2025-09-14T10:25:24.338077Z",
     "iopub.status.idle": "2025-09-14T10:25:24.367670Z",
     "shell.execute_reply": "2025-09-14T10:25:24.366590Z"
    },
    "papermill": {
     "duration": 0.040268,
     "end_time": "2025-09-14T10:25:24.369329",
     "exception": false,
     "start_time": "2025-09-14T10:25:24.329061",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_time</th>\n",
       "      <th>event_type</th>\n",
       "      <th>product_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>category_code</th>\n",
       "      <th>brand</th>\n",
       "      <th>price</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_session</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-10-01 00:00:00+00:00</td>\n",
       "      <td>cart</td>\n",
       "      <td>5773203</td>\n",
       "      <td>1487580005134238553</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>runail</td>\n",
       "      <td>2.62</td>\n",
       "      <td>463240011</td>\n",
       "      <td>26dd6e6e-4dac-4778-8d2c-92e149dab885</td>\n",
       "      <td>2019-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-10-01 00:00:03+00:00</td>\n",
       "      <td>cart</td>\n",
       "      <td>5773353</td>\n",
       "      <td>1487580005134238553</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>runail</td>\n",
       "      <td>2.62</td>\n",
       "      <td>463240011</td>\n",
       "      <td>26dd6e6e-4dac-4778-8d2c-92e149dab885</td>\n",
       "      <td>2019-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-10-01 00:00:07+00:00</td>\n",
       "      <td>cart</td>\n",
       "      <td>5881589</td>\n",
       "      <td>2151191071051219817</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>lovely</td>\n",
       "      <td>13.48</td>\n",
       "      <td>429681830</td>\n",
       "      <td>49e8d843-adf3-428b-a2c3-fe8bc6a307c9</td>\n",
       "      <td>2019-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-10-01 00:00:07+00:00</td>\n",
       "      <td>cart</td>\n",
       "      <td>5723490</td>\n",
       "      <td>1487580005134238553</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>runail</td>\n",
       "      <td>2.62</td>\n",
       "      <td>463240011</td>\n",
       "      <td>26dd6e6e-4dac-4778-8d2c-92e149dab885</td>\n",
       "      <td>2019-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-10-01 00:00:15+00:00</td>\n",
       "      <td>cart</td>\n",
       "      <td>5881449</td>\n",
       "      <td>1487580013522845895</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>lovely</td>\n",
       "      <td>0.56</td>\n",
       "      <td>429681830</td>\n",
       "      <td>49e8d843-adf3-428b-a2c3-fe8bc6a307c9</td>\n",
       "      <td>2019-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 event_time event_type  product_id          category_id  \\\n",
       "0 2019-10-01 00:00:00+00:00       cart     5773203  1487580005134238553   \n",
       "1 2019-10-01 00:00:03+00:00       cart     5773353  1487580005134238553   \n",
       "2 2019-10-01 00:00:07+00:00       cart     5881589  2151191071051219817   \n",
       "3 2019-10-01 00:00:07+00:00       cart     5723490  1487580005134238553   \n",
       "4 2019-10-01 00:00:15+00:00       cart     5881449  1487580013522845895   \n",
       "\n",
       "  category_code   brand  price    user_id  \\\n",
       "0          <NA>  runail   2.62  463240011   \n",
       "1          <NA>  runail   2.62  463240011   \n",
       "2          <NA>  lovely  13.48  429681830   \n",
       "3          <NA>  runail   2.62  463240011   \n",
       "4          <NA>  lovely   0.56  429681830   \n",
       "\n",
       "                           user_session year_month  \n",
       "0  26dd6e6e-4dac-4778-8d2c-92e149dab885    2019-10  \n",
       "1  26dd6e6e-4dac-4778-8d2c-92e149dab885    2019-10  \n",
       "2  49e8d843-adf3-428b-a2c3-fe8bc6a307c9    2019-10  \n",
       "3  26dd6e6e-4dac-4778-8d2c-92e149dab885    2019-10  \n",
       "4  49e8d843-adf3-428b-a2c3-fe8bc6a307c9    2019-10  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ace30bcc",
   "metadata": {
    "papermill": {
     "duration": 0.008143,
     "end_time": "2025-09-14T10:25:24.387077",
     "exception": false,
     "start_time": "2025-09-14T10:25:24.378934",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 1.2 数据清洗 / Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84313843",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:25:24.405463Z",
     "iopub.status.busy": "2025-09-14T10:25:24.405062Z",
     "iopub.status.idle": "2025-09-14T10:25:35.926083Z",
     "shell.execute_reply": "2025-09-14T10:25:35.925015Z"
    },
    "papermill": {
     "duration": 11.532003,
     "end_time": "2025-09-14T10:25:35.927591",
     "exception": false,
     "start_time": "2025-09-14T10:25:24.395588",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除重复行 / Duplicates removed: 1,120,960\n"
     ]
    }
   ],
   "source": [
    "# === 1.2.1 重复值检查 / Duplicates ===========================================\n",
    "dedup_keys = [\"event_time\", \"user_id\", \"product_id\", \"event_type\"]\n",
    "before_dupes = df_raw.shape[0]\n",
    "df_raw.drop_duplicates(subset=dedup_keys, keep=\"first\", inplace=True)\n",
    "print(f\"删除重复行 / Duplicates removed: {before_dupes - df_raw.shape[0]:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce70cbf5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:25:35.945401Z",
     "iopub.status.busy": "2025-09-14T10:25:35.945032Z",
     "iopub.status.idle": "2025-09-14T10:26:08.284743Z",
     "shell.execute_reply": "2025-09-14T10:26:08.283662Z"
    },
    "papermill": {
     "duration": 32.350707,
     "end_time": "2025-09-14T10:26:08.286548",
     "exception": false,
     "start_time": "2025-09-14T10:25:35.935841",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category_code: fill 1786 missing values\n",
      "brand: fill 1786 missing values\n"
     ]
    }
   ],
   "source": [
    "# === 1.2.2 缺失值处理 / Missing Values ======================================\n",
    "# -----------------------------\n",
    "# 1) 构建映射字典 / Build mapping dicts\n",
    "# -----------------------------\n",
    "def build_mode_mapping(df, key_col, value_col):\n",
    "    \"\"\"\n",
    "    返回: {product_id: 最常出现的value}\n",
    "    Return a dict: product_id -> most frequent value for that column.\n",
    "    \"\"\"\n",
    "    tmp = (\n",
    "        df.dropna(subset=[key_col, value_col])\n",
    "          .groupby(key_col)[value_col]\n",
    "          .agg(lambda x: x.mode().iloc[0])  # 取众数 / take mode\n",
    "    )\n",
    "    return tmp.to_dict()\n",
    "\n",
    "prod2cat = build_mode_mapping(df_raw, \"product_id\", \"category_code\")\n",
    "prod2brand = build_mode_mapping(df_raw, \"product_id\", \"brand\")\n",
    "\n",
    "\n",
    "# -----------------------------\n",
    "# 2) 回填缺失 / Fill missing using mapping\n",
    "# -----------------------------\n",
    "# category_code\n",
    "mask_cat_na = df_raw[\"category_code\"].isna() | (df_raw[\"category_code\"] == \"\")\n",
    "before_fill_cat = mask_cat_na.sum()\n",
    "df_raw.loc[mask_cat_na, \"category_code\"] = (\n",
    "    df_raw.loc[mask_cat_na, \"product_id\"].map(prod2cat)\n",
    ")\n",
    "\n",
    "after_fill_cat = df_raw[\"category_code\"].isna().sum() + (df_raw[\"category_code\"] == \"\").sum()\n",
    "filled_cat = before_fill_cat - after_fill_cat\n",
    "print(f\"category_code: fill {filled_cat} missing values\")\n",
    "\n",
    "# brand\n",
    "mask_brand_na = df_raw[\"brand\"].isna() | (df_raw[\"brand\"] == \"\")\n",
    "before_fill_brand = mask_brand_na.sum()\n",
    "df_raw.loc[mask_brand_na, \"brand\"] = (\n",
    "    df_raw.loc[mask_brand_na, \"product_id\"].map(prod2brand)\n",
    ")\n",
    "\n",
    "after_fill_brand = df_raw[\"brand\"].isna().sum() + (df_raw[\"brand\"] == \"\").sum()\n",
    "filled_brand = before_fill_brand - after_fill_brand\n",
    "print(f\"brand: fill {filled_cat} missing values\")\n",
    "\n",
    "\n",
    "# -----------------------------\n",
    "# 3) 统一剩余缺失值 / Fill remaining NAs with fallback labels\n",
    "# -----------------------------\n",
    "df_raw[\"category_code\"] = df_raw[\"category_code\"].fillna(\"beauty_unknown\")\n",
    "df_raw[\"brand\"] = df_raw[\"brand\"].fillna(\"unknown_brand\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eb25895",
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     "start_time": "2025-09-14T10:26:08.295473",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "[可选步骤] 移除时区信息 / [Optional Step] Remove timezone information (if present)\n",
    "\n",
    "在电商数据分析中，我们通常按天、周、月聚合数据（如 GMV、留存率等）。时区对这些周期性统计结果影响很小，特别是当数据源已经标准化到 UTC 时。因此，为了简化后续操作，我们可以去掉时区信息。\n",
    "\n",
    "In e-commerce data analysis, we usually aggregate data by day, week, or month (e.g., GMV, retention rates). Timezone differences have little impact on these metrics, especially if the data is already standardized to UTC. Therefore, we can safely remove the timezone information to simplify processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b68728a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:08.432944Z",
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   "outputs": [],
   "source": [
    "# Optional\n",
    "df['event_time'] = df['event_time'].dt.tz_localize(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "639729aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:08.482976Z",
     "iopub.status.busy": "2025-09-14T10:26:08.482666Z",
     "iopub.status.idle": "2025-09-14T10:26:17.823741Z",
     "shell.execute_reply": "2025-09-14T10:26:17.822835Z"
    },
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     "duration": 9.352137,
     "end_time": "2025-09-14T10:26:17.825548",
     "exception": false,
     "start_time": "2025-09-14T10:26:08.473411",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_13/3471177408.py:5: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  df_raw[\"event_month\"] = df_raw[\"event_time\"].dt.to_period(\"M\")\n"
     ]
    }
   ],
   "source": [
    "# === 1.2.3 时间解析 & 衍生列 / Time Parsing & Derived Columns ================\n",
    "# event_date: 将时间截断到“天”，但保留 datetime64 dtype\n",
    "# Normalize to midnight, keep dtype = datetime64[ns].\n",
    "df_raw[\"event_date\"] = df_raw[\"event_time\"].dt.normalize()  \n",
    "df_raw[\"event_month\"] = df_raw[\"event_time\"].dt.to_period(\"M\")  \n",
    "df_raw[\"event_weekday\"] = df_raw[\"event_time\"].dt.day_name()\n",
    "df_raw[\"hour_bin\"] = df_raw[\"event_time\"].dt.hour // 4  # 6 个 4h 时段 / six 4‑h bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "475ecc92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:17.844107Z",
     "iopub.status.busy": "2025-09-14T10:26:17.843759Z",
     "iopub.status.idle": "2025-09-14T10:26:24.541222Z",
     "shell.execute_reply": "2025-09-14T10:26:24.540541Z"
    },
    "papermill": {
     "duration": 6.708509,
     "end_time": "2025-09-14T10:26:24.542964",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# === 1.2.4 行为编码 / Event Encoding ========================================\n",
    "event_dummies = pd.get_dummies(df_raw[\"event_type\"], prefix=\"is\")\n",
    "df_raw = pd.concat([df_raw, event_dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "37bcb026",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:24.562028Z",
     "iopub.status.busy": "2025-09-14T10:26:24.561188Z",
     "iopub.status.idle": "2025-09-14T10:26:30.595653Z",
     "shell.execute_reply": "2025-09-14T10:26:30.594619Z"
    },
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     "duration": 6.045639,
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     "exception": false,
     "start_time": "2025-09-14T10:26:24.551694",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of negative price records: 124\n"
     ]
    }
   ],
   "source": [
    "# === 1.2.5 清理无效价格 / Remove Invalid Prices ==============================\n",
    "# 检查是否存在价格小于0的异常记录 / Check for abnormal records where price < 0\n",
    "negative_prices = df_raw[df_raw['price'] < 0]\n",
    "print(f\"Number of negative price records: {len(negative_prices)}\")\n",
    "\n",
    "# 删除价格为负的记录，并重置索引 / Remove records with negative price values and reset the index\n",
    "df_raw = df_raw[df_raw['price'] >= 0].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8ea87ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:30.615732Z",
     "iopub.status.busy": "2025-09-14T10:26:30.614915Z",
     "iopub.status.idle": "2025-09-14T10:26:30.634720Z",
     "shell.execute_reply": "2025-09-14T10:26:30.633819Z"
    },
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     "duration": 0.030296,
     "end_time": "2025-09-14T10:26:30.636079",
     "exception": false,
     "start_time": "2025-09-14T10:26:30.605783",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>event_time</th>\n",
       "      <th>event_type</th>\n",
       "      <th>product_id</th>\n",
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       "      <th>hour_bin</th>\n",
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       "      <th>is_view</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>2019-10-01 00:00:00+00:00</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-10-01 00:00:03+00:00</td>\n",
       "      <td>cart</td>\n",
       "      <td>5773353</td>\n",
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       "      <td>beauty_unknown</td>\n",
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       "      <td>463240011</td>\n",
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       "      <td>2019-10</td>\n",
       "      <td>2019-10-01 00:00:00+00:00</td>\n",
       "      <td>2019-10</td>\n",
       "      <td>Tuesday</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>2019-10-01 00:00:07+00:00</td>\n",
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       "      <td>2151191071051219817</td>\n",
       "      <td>beauty_unknown</td>\n",
       "      <td>lovely</td>\n",
       "      <td>13.48</td>\n",
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       "      <td>2019-10</td>\n",
       "      <td>2019-10-01 00:00:00+00:00</td>\n",
       "      <td>2019-10</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-10-01 00:00:07+00:00</td>\n",
       "      <td>cart</td>\n",
       "      <td>5723490</td>\n",
       "      <td>1487580005134238553</td>\n",
       "      <td>beauty_unknown</td>\n",
       "      <td>runail</td>\n",
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       "      <td>2019-10</td>\n",
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       "      <td>1487580013522845895</td>\n",
       "      <td>beauty_unknown</td>\n",
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       "      <td>429681830</td>\n",
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       "      <td>2019-10</td>\n",
       "      <td>2019-10-01 00:00:00+00:00</td>\n",
       "      <td>2019-10</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
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       "                 event_time event_type  product_id          category_id  \\\n",
       "0 2019-10-01 00:00:00+00:00       cart     5773203  1487580005134238553   \n",
       "1 2019-10-01 00:00:03+00:00       cart     5773353  1487580005134238553   \n",
       "2 2019-10-01 00:00:07+00:00       cart     5881589  2151191071051219817   \n",
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       "4 2019-10-01 00:00:15+00:00       cart     5881449  1487580013522845895   \n",
       "\n",
       "    category_code   brand  price    user_id  \\\n",
       "0  beauty_unknown  runail   2.62  463240011   \n",
       "1  beauty_unknown  runail   2.62  463240011   \n",
       "2  beauty_unknown  lovely  13.48  429681830   \n",
       "3  beauty_unknown  runail   2.62  463240011   \n",
       "4  beauty_unknown  lovely   0.56  429681830   \n",
       "\n",
       "                           user_session year_month                event_date  \\\n",
       "0  26dd6e6e-4dac-4778-8d2c-92e149dab885    2019-10 2019-10-01 00:00:00+00:00   \n",
       "1  26dd6e6e-4dac-4778-8d2c-92e149dab885    2019-10 2019-10-01 00:00:00+00:00   \n",
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       "4  49e8d843-adf3-428b-a2c3-fe8bc6a307c9    2019-10 2019-10-01 00:00:00+00:00   \n",
       "\n",
       "  event_month event_weekday  hour_bin  is_cart  is_purchase  \\\n",
       "0     2019-10       Tuesday         0     True        False   \n",
       "1     2019-10       Tuesday         0     True        False   \n",
       "2     2019-10       Tuesday         0     True        False   \n",
       "3     2019-10       Tuesday         0     True        False   \n",
       "4     2019-10       Tuesday         0     True        False   \n",
       "\n",
       "   is_remove_from_cart  is_view  \n",
       "0                False    False  \n",
       "1                False    False  \n",
       "2                False    False  \n",
       "3                False    False  \n",
       "4                False    False  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55ee5bed",
   "metadata": {
    "papermill": {
     "duration": 0.008262,
     "end_time": "2025-09-14T10:26:30.653179",
     "exception": false,
     "start_time": "2025-09-14T10:26:30.644917",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 2. 用户价值分层 / Customer Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a56d2e",
   "metadata": {
    "papermill": {
     "duration": 0.00811,
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     "start_time": "2025-09-14T10:26:30.661611",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2.1 RFM & 行为特征工程 / RFM + Behavioural Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f9406ef",
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   "outputs": [
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       "         recency_days  frequency  monetary  view_cnt  cart_cnt  remove_cnt  \\\n",
       "user_id                                                                      \n",
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       "1180452           0.0  \n",
       "1458813           0.0  \n",
       "2038666           0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 仅保留购买事件用于 RFM / Keep purchases for RFM\n",
    "purchase_df = df_raw[df_raw['is_purchase'] == 1]\n",
    "\n",
    "# 参考日期 / Reference date for Recency calculation\n",
    "ref_date = pd.Timestamp('2020-03-01', tz='UTC')\n",
    "\n",
    "# --- 构建 RFM / Build RFM ---------------------------\n",
    "rfm = purchase_df.groupby('user_id').agg(\n",
    "    recency_days = ('event_time', lambda x: (ref_date - x.max()).days),\n",
    "    frequency = ('event_time', 'count'),\n",
    "    monetary = ('price', 'sum')\n",
    ")\n",
    "\n",
    "# --- 行为计数特征 / Behaviour count features --------\n",
    "behaviour = df_raw.groupby('user_id').agg(\n",
    "    view_cnt = ('is_view', 'sum'),\n",
    "    cart_cnt = ('is_cart', 'sum'),\n",
    "    remove_cnt = ('is_remove_from_cart', 'sum'),\n",
    "    purchase_cnt = ('is_purchase', 'sum')\n",
    ")\n",
    "\n",
    "# 合并 / Combine\n",
    "features = rfm.join(behaviour, how='outer').fillna(0)\n",
    "\n",
    "# 日志缩放 + Min-Max / Log transform & Min-Max scaling\n",
    "log_cols = ['recency_days','frequency','monetary','view_cnt','cart_cnt','remove_cnt','purchase_cnt']\n",
    "features_log = features[log_cols].apply(lambda x: np.log1p(x))\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "features_scaled = scaler.fit_transform(features_log)\n",
    "features_scaled = pd.DataFrame(features_scaled, index=features.index, columns=log_cols)\n",
    "features_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a283b19c",
   "metadata": {
    "papermill": {
     "duration": 0.008373,
     "end_time": "2025-09-14T10:26:45.700963",
     "exception": false,
     "start_time": "2025-09-14T10:26:45.692590",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2.2 K-means 聚类 / K-means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5dbed0e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:45.719833Z",
     "iopub.status.busy": "2025-09-14T10:26:45.719102Z",
     "iopub.status.idle": "2025-09-14T10:26:45.723325Z",
     "shell.execute_reply": "2025-09-14T10:26:45.722596Z"
    },
    "papermill": {
     "duration": 0.01522,
     "end_time": "2025-09-14T10:26:45.724688",
     "exception": false,
     "start_time": "2025-09-14T10:26:45.709468",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # =============================================================================\n",
    "# # 2.2.1 MiniBatchKMeans\n",
    "# # =============================================================================\n",
    "# inertia_list = []   # SSE / Inertia for Elbow\n",
    "# # silhouette_list = []   # Silhouette scores\n",
    "# model_dict = {}   # 保存各 k 的模型 / store models keyed by k\n",
    "\n",
    "# k_range = range(3, 9)  # 尝试 3-8 个簇 / Try 3–8 clusters\n",
    "# for k in tqdm(k_range, desc=\"MiniBatchKMeans\"):\n",
    "#     mbkm = MiniBatchKMeans(\n",
    "#         n_clusters=k,\n",
    "#         batch_size=16384,\n",
    "#         max_iter=200,\n",
    "#         n_init=10,\n",
    "#         random_state=42,\n",
    "#         verbose=0\n",
    "#     )\n",
    "#     mbkm.fit(features_scaled)\n",
    "    \n",
    "#     # 保存指标 / Save metrics\n",
    "#     inertia_list.append(mbkm.inertia_)\n",
    "#     # sil_score = silhouette_score(features_scaled, mbkm.labels_)\n",
    "#     # silhouette_list.append(sil_score)\n",
    "    \n",
    "#     # 缓存模型 / Cache the trained model\n",
    "#     model_dict[k] = mbkm\n",
    "\n",
    "# print(\"All models trained & cached!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b94b996",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:45.743996Z",
     "iopub.status.busy": "2025-09-14T10:26:45.743718Z",
     "iopub.status.idle": "2025-09-14T10:26:52.595693Z",
     "shell.execute_reply": "2025-09-14T10:26:52.594655Z"
    },
    "papermill": {
     "duration": 6.863776,
     "end_time": "2025-09-14T10:26:52.597402",
     "exception": false,
     "start_time": "2025-09-14T10:26:45.733626",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KMeans: 100%|██████████| 6/6 [00:06<00:00,  1.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All models trained & cached!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# =============================================================================\n",
    "# 2.2.1 KMeans\n",
    "# =============================================================================\n",
    "inertia_list = []   # SSE / Inertia for Elbow\n",
    "# silhouette_list = []   # Silhouette scores\n",
    "model_dict = {}   # 保存各 k 的模型 / store models keyed by k\n",
    "\n",
    "k_range = range(3, 9)  # 尝试 3-8 个簇 / Try 3–8 clusters\n",
    "for k in tqdm(k_range, desc=\"KMeans\"):\n",
    "    km = KMeans(n_clusters=k, random_state=42, n_init='auto')\n",
    "    km.fit(features_scaled)\n",
    "    \n",
    "    # 保存指标 / Save metrics\n",
    "    inertia_list.append(km.inertia_)\n",
    "    # sil_score = silhouette_score(features_scaled, km.labels_)\n",
    "    # silhouette_list.append(sil_score)\n",
    "    \n",
    "    # 缓存模型 / Cache the trained model\n",
    "    model_dict[k] = km\n",
    "\n",
    "print(\"All models trained & cached!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16320870",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:52.617309Z",
     "iopub.status.busy": "2025-09-14T10:26:52.616957Z",
     "iopub.status.idle": "2025-09-14T10:26:52.882144Z",
     "shell.execute_reply": "2025-09-14T10:26:52.881369Z"
    },
    "papermill": {
     "duration": 0.276902,
     "end_time": "2025-09-14T10:26:52.883836",
     "exception": false,
     "start_time": "2025-09-14T10:26:52.606934",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2.2.2 寻找最佳 K / Find Best K\n",
    "# --- 绘制 Elbow 曲线 / Plotting Elbow Curve -----------------\n",
    "plt.figure()\n",
    "plt.plot(list(k_range), inertia_list, marker='o')\n",
    "plt.title('Elbow Method: Inertia vs. k')\n",
    "plt.xlabel('Number of clusters (k)')\n",
    "plt.ylabel('SSE / Inertia')\n",
    "plt.xticks(list(k_range))\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# # --- 绘制 Silhouette 分数 / Plotting Silhouette Scores -------\n",
    "# plt.figure()\n",
    "# plt.plot(list(k_range), silhouette_list, marker='o')\n",
    "# plt.title('Silhouette Score vs. k')\n",
    "# plt.xlabel('Number of clusters (k)')\n",
    "# plt.ylabel('Silhouette Score')\n",
    "# plt.xticks(list(k_range))\n",
    "# plt.grid(True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42392ee",
   "metadata": {
    "papermill": {
     "duration": 0.009586,
     "end_time": "2025-09-14T10:26:52.903613",
     "exception": false,
     "start_time": "2025-09-14T10:26:52.894027",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "从 Elbow 曲线来看，K = 6 是最佳选择\n",
    "\n",
    "---\n",
    "\n",
    "Based on the Elbow curve, K = 6 is the optimal choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8847e93e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:52.924903Z",
     "iopub.status.busy": "2025-09-14T10:26:52.924591Z",
     "iopub.status.idle": "2025-09-14T10:26:58.736091Z",
     "shell.execute_reply": "2025-09-14T10:26:58.735083Z"
    },
    "papermill": {
     "duration": 5.824363,
     "end_time": "2025-09-14T10:26:58.737876",
     "exception": false,
     "start_time": "2025-09-14T10:26:52.913513",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# best_k = k_range[int(np.argmax(silhouette_list))]\n",
    "# print(f\"Best k by silhouette score: {best_k}\")\n",
    "best_k = 6\n",
    "\n",
    "# 直接复用已缓存模型 / Retrieve the cached best model\n",
    "final_km = model_dict[best_k]\n",
    "\n",
    "# 将簇标签添加到特征表 / Attach cluster labels\n",
    "features_scaled['cluster'] = final_km.labels_\n",
    "\n",
    "# 保存簇标签至数据 / Map cluster back to df_raw\n",
    "df_raw = df_raw.merge(\n",
    "    features_scaled['cluster'],\n",
    "    left_on='user_id',\n",
    "    right_index=True,\n",
    "    how='left'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ddcd2ccf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:58.759581Z",
     "iopub.status.busy": "2025-09-14T10:26:58.759109Z",
     "iopub.status.idle": "2025-09-14T10:26:58.775244Z",
     "shell.execute_reply": "2025-09-14T10:26:58.774173Z"
    },
    "papermill": {
     "duration": 0.028965,
     "end_time": "2025-09-14T10:26:58.777000",
     "exception": false,
     "start_time": "2025-09-14T10:26:58.748035",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved scaler.pkl, kmeans_model.pkl to disk.\n"
     ]
    }
   ],
   "source": [
    "# 2.2.3 保存 Scaler 和 KMeans 模型 / Save Scaler and KMeans Model\n",
    "# 保存 StandardScaler 以便在线推断 / Save scaler for online inference\n",
    "joblib.dump(scaler, '/kaggle/working/scaler.pkl')\n",
    "\n",
    "# 保存 KMeans 聚类模型 / Save KMeans clustering model\n",
    "joblib.dump(final_km, '/kaggle/working/kmeans_model.pkl')\n",
    "\n",
    "# 保存成功提示 / Confirmation\n",
    "print(\"Saved scaler.pkl, kmeans_model.pkl to disk.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f0bed785",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:58.799450Z",
     "iopub.status.busy": "2025-09-14T10:26:58.799104Z",
     "iopub.status.idle": "2025-09-14T10:26:58.977922Z",
     "shell.execute_reply": "2025-09-14T10:26:58.977043Z"
    },
    "papermill": {
     "duration": 0.191918,
     "end_time": "2025-09-14T10:26:58.979487",
     "exception": false,
     "start_time": "2025-09-14T10:26:58.787569",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_cnt</th>\n",
       "      <th>user_pct</th>\n",
       "      <th>recency_days_mean</th>\n",
       "      <th>frequency_mean</th>\n",
       "      <th>monetary_mean</th>\n",
       "      <th>view_cnt_mean</th>\n",
       "      <th>cart_cnt_mean</th>\n",
       "      <th>remove_cnt_mean</th>\n",
       "      <th>purchase_cnt_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42326</td>\n",
       "      <td>0.03</td>\n",
       "      <td>59.77</td>\n",
       "      <td>23.18</td>\n",
       "      <td>102.66</td>\n",
       "      <td>80.46</td>\n",
       "      <td>66.34</td>\n",
       "      <td>42.00</td>\n",
       "      <td>23.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1112742</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>160553</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.10</td>\n",
       "      <td>4.87</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52816</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.36</td>\n",
       "      <td>29.42</td>\n",
       "      <td>27.16</td>\n",
       "      <td>17.57</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>204606</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>9.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>66315</td>\n",
       "      <td>0.04</td>\n",
       "      <td>78.01</td>\n",
       "      <td>4.49</td>\n",
       "      <td>29.53</td>\n",
       "      <td>11.78</td>\n",
       "      <td>8.09</td>\n",
       "      <td>2.92</td>\n",
       "      <td>4.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_cnt  user_pct  recency_days_mean  frequency_mean  monetary_mean  \\\n",
       "cluster                                                                         \n",
       "0           42326      0.03              59.77           23.18         102.66   \n",
       "1         1112742      0.68               0.00            0.00           0.00   \n",
       "2          160553      0.10               0.01            0.02           0.12   \n",
       "3           52816      0.03               0.02            0.07           0.36   \n",
       "4          204606      0.12               0.00            0.00           0.02   \n",
       "5           66315      0.04              78.01            4.49          29.53   \n",
       "\n",
       "         view_cnt_mean  cart_cnt_mean  remove_cnt_mean  purchase_cnt_mean  \n",
       "cluster                                                                    \n",
       "0                80.46          66.34            42.00              23.18  \n",
       "1                 1.37           0.04             0.01               0.00  \n",
       "2                 3.10           4.87             0.41               0.02  \n",
       "3                29.42          27.16            17.57               0.07  \n",
       "4                 9.24           0.24             0.05               0.00  \n",
       "5                11.78           8.09             2.92               4.49  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.2.4 簇特征概览 / Cluster Feature Overview\n",
    "\n",
    "# 1) 将簇标签合并回原始特征 / Attach cluster label to raw features\n",
    "features_with_cluster = features.join(features_scaled['cluster'])\n",
    "\n",
    "# 2) 计算簇规模 / Cluster size\n",
    "cluster_size = features_with_cluster['cluster'].value_counts().rename('user_cnt')\n",
    "cluster_pct  = (cluster_size / cluster_size.sum()).rename('user_pct')\n",
    "\n",
    "# 3) 计算各特征的中位数 & 均值 / Median & Mean per feature\n",
    "# agg_median = features_with_cluster.groupby('cluster')[log_cols].median().add_suffix('_median')\n",
    "agg_mean   = features_with_cluster.groupby('cluster')[log_cols].mean().add_suffix('_mean')\n",
    "\n",
    "# 4) 合并成画像表 / Combine into a profiling table\n",
    "# cluster_profile = pd.concat([cluster_size, cluster_pct, agg_mean, agg_median], axis=1).sort_index()\n",
    "# cluster_profile = pd.concat([cluster_size, cluster_pct, agg_median], axis=1).sort_index()\n",
    "cluster_profile = pd.concat([cluster_size, cluster_pct, agg_mean], axis=1).sort_index()\n",
    "cluster_profile.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2487714f",
   "metadata": {
    "papermill": {
     "duration": 0.010196,
     "end_time": "2025-09-14T10:26:58.999964",
     "exception": false,
     "start_time": "2025-09-14T10:26:58.989768",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 3. 分层留存分析 / Segment-level Retention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8f5ef84b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:59.022979Z",
     "iopub.status.busy": "2025-09-14T10:26:59.022339Z",
     "iopub.status.idle": "2025-09-14T10:26:59.027034Z",
     "shell.execute_reply": "2025-09-14T10:26:59.026138Z"
    },
    "papermill": {
     "duration": 0.017886,
     "end_time": "2025-09-14T10:26:59.028572",
     "exception": false,
     "start_time": "2025-09-14T10:26:59.010686",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 3.0 把 cluster 标签合并到主表 / Attach cluster to main DF\n",
    "# all_features_scaled = pd.concat(\n",
    "#     [features_scaled, features_test_scaled], axis=0\n",
    "# )\n",
    "\n",
    "# cluster_map = all_features_scaled[\"cluster\"]\n",
    "\n",
    "# df_raw = df_raw.merge(\n",
    "#     cluster_map.rename(\"cluster\"),\n",
    "#     left_on=\"user_id\",\n",
    "#     right_index=True,\n",
    "#     how=\"left\",\n",
    "# )\n",
    "\n",
    "# print(\n",
    "#     \"Cluster label distribution (all data):\\n\",\n",
    "#     df_raw[\"cluster\"].value_counts(normalize=True).round(3),\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb39cc2c",
   "metadata": {
    "papermill": {
     "duration": 0.010099,
     "end_time": "2025-09-14T10:26:59.048923",
     "exception": false,
     "start_time": "2025-09-14T10:26:59.038824",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3.1 Cohort 构建 / Cohort Construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "350f1417",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:26:59.071121Z",
     "iopub.status.busy": "2025-09-14T10:26:59.070811Z",
     "iopub.status.idle": "2025-09-14T10:27:04.457149Z",
     "shell.execute_reply": "2025-09-14T10:27:04.456366Z"
    },
    "papermill": {
     "duration": 5.399179,
     "end_time": "2025-09-14T10:27:04.458864",
     "exception": false,
     "start_time": "2025-09-14T10:26:59.059685",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_13/1450132686.py:9: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  purchase['purchase_month'] = purchase['event_date'].dt.to_period('M')\n"
     ]
    }
   ],
   "source": [
    "# 仅保留购买事件 / Keep purchase events only\n",
    "purchase = df_raw[df_raw['is_purchase'] == 1].copy()\n",
    "\n",
    "# 计算首购月份 / First purchase month per user\n",
    "first_purchase_month = purchase.groupby('user_id')['event_month'].min()\n",
    "\n",
    "# 打标签 / Add cohort & purchase month\n",
    "purchase['cohort_month']   = purchase['user_id'].map(first_purchase_month)\n",
    "purchase['purchase_month'] = purchase['event_date'].dt.to_period('M')\n",
    "# 计算距 cohort 的月份差 / Period index\n",
    "purchase['period_index']   = (purchase['purchase_month'] - purchase['cohort_month']).apply(lambda x: x.n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2872bf2b",
   "metadata": {
    "papermill": {
     "duration": 0.010314,
     "end_time": "2025-09-14T10:27:04.479975",
     "exception": false,
     "start_time": "2025-09-14T10:27:04.469661",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3.2 留存热力图 / Retention Heatmap by Segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2e6d1c93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:27:04.502228Z",
     "iopub.status.busy": "2025-09-14T10:27:04.501914Z",
     "iopub.status.idle": "2025-09-14T10:27:06.426179Z",
     "shell.execute_reply": "2025-09-14T10:27:06.425311Z"
    },
    "papermill": {
     "duration": 1.937201,
     "end_time": "2025-09-14T10:27:06.427698",
     "exception": false,
     "start_time": "2025-09-14T10:27:04.490497",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZWQgKCsKuXbtgYWEBT09PALkjgjNnzoSvr69kmT9tbW08evQIBw4cwNChQzFu3Dioqalh2rRpGDVqFFq2bAl3d3fExsZi06ZNqF69usxGkIsSU1H71NPTw+rVq6GtrQ1NTU04OjrC0tIy3/bDhg1Deno6XF1dUaVKFcTHx2P79u24f/8+Fi5cKDWtpbDLAgLA+PHjcffuXSxcuBDnz59H9+7dYWJigvj4eBw8eBAhISEICgoCAEyaNAk7d+5E+/btMXr0aFSsWBGbN2/Go0ePsG/fviJP91BVVcXcuXPh6emJZs2aoXfv3pJlAS0sLODt7V2ofhwcHLBr1y74+PigUaNG0NLSQqdOnYoUCwC0b98ednZ2WLRoEby8vNCsWTMMGzYMAQEBCA8PR5s2baCqqooHDx5gz549WLp0Kbp37y6JYdWqVZg5cyasrKxgZGSEli1bYvz48Th06BB++OEHDBgwAA4ODnj79i1u376NvXv3IjY2FgYGBkWK08HBAQAwevRotG3bFsrKyujVq1e+bWfOnInTp0/DxcUFI0aMgIqKCtasWYPMzEypteGJiGSmxNdFISKJ48ePiwcOHCi2trYWa2lpidXU1MRWVlbiUaNGiRMSEvK037dvn9jFxUWsqakp1tTUFFtbW4u9vLzEkZGRUu2WLVsmNjc3FwuFQnHjxo3Fly9fFjs4OIjbtWsnafNxmbo9e/ZI7ftxKbRr165Jlfv5+YkBiJOSkoocU7NmzcS1a9fOcz79+/fPs3TdX3/9Jba1tRWrqKh8cRm/nTt3it3c3MTGxsZiFRUVsb6+vtjNzU38119/5Wlb2GUB/2vv3r3iNm3aiCtWrChWUVERm5qainv27Cm+cOGCVLuYmBhx9+7dxXp6euIKFSqIGzduLD5y5IhUm89d749L3/1/XLt27RLXr19fLBQKxRUrVhT36dNH/PTpU6k2/fv3F2tqauYb+5s3b8Q///yzWE9PTwzgi0sEmpubizt27Jhv3cdlJf8b4x9//CF2cHAQq6uri7W1tcX29vbiCRMmiJ8/fy5pEx8fL+7YsaNYW1s7z7KUr1+/Fvv6+oqtrKzEampqYgMDA7Gzs7N4wYIFkmUqP16b+fPn54kJ/7fUX3Z2tnjUqFFiQ0NDsUAgkFoi8P/bisVi8fXr18Vt27YVa2lpiTU0NMQtWrQQBwUFSbX53L+Fj/fy/Pnz+V4vIqL/JxCL+dQHkaITiUQwNDRE165dJVMDiIiIqGRwDjeRgnn//n2eecdbtmxBcnKy1Ku1iYiIqGRwhJtIwVy4cAHe3t7o0aMHKlWqhOvXr2P9+vWwsbFBWFiY1JrKREREVPz40CSRgrGwsEDVqlWxbNkyJCcno2LFivDw8MCcOXOYbBMREckBp5QQKRgLCwscOnQI8fHxyMrKQnx8PDZs2PDZNwMSERGVF3///Tc6deqEypUrQyAQ4ODBg1/c58KFC2jQoAGEQiGsrKywadOmIh+XCTcRERERlQtv375F3bp1sXLlykK1f/ToETp27IgWLVogPDwcY8aMweDBg/N9j0BBOIebiIiIiModgUCAAwcOoEuXLp9tM3HiRBw9ehR37tyRlPXq1Qupqak4ceJEoY/FEW4iIiIiKrMyMzORnp4utf33Tb3fIjg4GG5ublJlbdu2lXq7cGEo5EOT6ma95R0CyVhsJO8pEVFJsKi1U94hkIxlxJWueyrrPG3iwFqYPn26VJmfnx+mTZv2zX3Hx8fD2NhYqszY2Bjp6enIyMiAurp6ofpRyISbiIiIiMoHX19f+Pj4SJUJhUI5RZM/JtxEREREVGIEAtnOaBYKhcWWYJuYmCAhIUGqLCEhATo6OoUe3QaYcBMRERFRCRKUoUcInZyccOzYMamy06dPw8nJqUj9lJ0zJiIiIiL6Bm/evEF4eDjCw8MB5C77Fx4ejri4OAC501M8PDwk7X/55Rc8fPgQEyZMwP379xEYGIjdu3fD29u7SMflCDcRERERlRhZTykpitDQULRo0ULy+ePc7/79+2PTpk148eKFJPkGAEtLSxw9ehTe3t5YunQpvvvuO6xbtw5t27Yt0nGZcBMRERFRudC8eXMU9Aqa/N4i2bx5c9y4ceObjsuEm4iIiIhKjDxHuOWFCTcRERERlRiBQCDvEEpc+fsTg4iIiIioBHGEm4iIiIhKUPkb72XCTUREREQlpjzO4S5/Z0xEREREVII4wk1EREREJYYj3EREREREJFNyH+F++fIlNmzYgODgYMTHxwMATExM4OzsjAEDBsDQ0FDOERIRERGRrAjK4XivXM/42rVrqFmzJpYtWwZdXV24urrC1dUVurq6WLZsGaytrREaGirPEImIiIhIhgQCJZluZYFcR7hHjRqFHj16YPXq1XkWQReLxfjll18watQoBAcHyylCIiIiIqJvI9eE++bNm9i0aVO+bxwSCATw9vZG/fr15RAZERERERWHsjIqLUtyPWMTExOEhIR8tj4kJATGxsYlGBERERERFSdOKSlh48aNw9ChQxEWFoZWrVpJkuuEhAScPXsWa9euxYIFC+QZIhERERHRN5Frwu3l5QUDAwMsXrwYgYGByMnJAQAoKyvDwcEBmzZtgru7uzxDJCIiIiIZEiDvVGJFJ/dlAXv27ImePXviw4cPePnyJQDAwMAAqqqqco6MiIiIiGStrEwDkSW5J9wfqaqqwtTUVN5hEBERERHJVKn+EyMmJgYtW7aUdxhEREREJCPl8aHJUh3lmzdvcPHiRXmHQURERET01eQ6pWTZsmUF1j979qyEIiEiIiKiklBWRqVlSa4J95gxY2Bqago1NbV867Oysko4IiIiIiIqXky4S5S5uTnmzp372aX/wsPD4eDgUMJRERERERHJjlz/xHBwcEBYWNhn6wUCAcRicQlGRERERETFqTw+NCnXEW5/f3+8e/fus/W2trZ49OhRCUZERERERMWprCTJsiTXhNvW1rbAelVVVZibm5dQNEREREREslfq/sSYM2cOUlNT5R0GERERERUDAZRkupUFpS7K2bNnIzk5Wd5hEBERERHJRKl5tftHfEiSiIiISHGVxznc5e+Mi0mTxtbYu2EcHl4LREbcTnRq0zBPmyk+3fEwNBDJUZtxdMdkVLcwkarX19XExqVeSLi7Hi9ur8OqeUOhqSEs8LhCoSoWz/DE05t/IOneRuxcPQZGBrpSbapWroT9GyfgVeQmPL6+GrMn/wxl5U+3vm5tCwQfC0DSvY3Yu2Ec9HU1JXXKykq4fHQWGtat/jWXRaGEhz3EpNEb8FPrGXCtNx7/nLvz2bYLZu6Da73x2L3tnwL73Lb+HIb+vBRtnX9H5xbTMHnMJsTFJkq1efbkJX7z3oROLaahXZPf4Td+K5JfvZbUZ2VlY+ZvO9Guye/4ufNchF6Jktp/56YLWDLnYNFPWMHxfioW3s+yjb9DyxeBQCDTrSwodQl3REREmXxQUlNDiNsRcRjz+4Z868cO74QRnu0w2nc9XDtPwdt3mTi8bRKEQlVJm43LRsKm5nf4oc9sdBs4Hy6O1lg5Z0iBx503tR86ujVAn+FL0cbdH6bG+vjzD29JvZKSAPs3TYCamgpa/OSHIT6r0LdHM0wd20PSJnDeEFwMugunDr7Q1dbAhJFdJHVjhnbEldAohN6M+corozjeZ2Shes3K8PbtUmC7v8/dRsStxzAw1Plin+FhMfippzNWbxmJRauHIjs7B2OHr0VGRu5LnzIysjB2+FpAIMCSP4Zh5SYvfPiQg0mjN0IkEgEADu+7gsh7T7Fq80h06uYIf98dkm+Knj9LxuH9VzFkZLtvO3kFxPupWHg/yzb+DiVFV2oS7ri4OFy9ehXx8fFl8qHJUxduYvqC3Th0MjTfeq9B7TF3+QEcOR2GO/fjMNg7EKZG+uj871/xtawqo22LehgxcS2uhccg6FokfKZuRo/OTjA11s+3Tx1tdQzo2QITZ2zFxaC7uHH7EYaOWwOnhrXQuL4VAMDNtQ5sanyHgb+uxK2Ixzh14Sb8F+7BMI82UFVVBgBYW1XBhp3nEP0oHrv/CkItqyoAAAszI/Tv2QJ+83bJ+nKVSd+7WGPIyHZwbWn/2TZJCWlYOucvTJn9M1RUlL/Y54LAIWj/YyNYWpnAqlZlTPbviYQXqYiMeAoAuH3jEeKfp2Cyf09Ur2GK6jVMMXlGT0RGPMX1kGgAwOOHiWjSrDYsrUzQtWcTpKa8RVrKWwDAoln78MuYDtDUqiCDK6BYeD8VC+9n2cbfoeVLeVyHW+5RBgYGwtzcHJaWlnB2dsb3338PIyMjuLi4FPhSnLLEwswIpkb6OHfp01ec6a8zcC08Bo4ONQAAjg1qIiXtDa7feihpc+7SbYhEYjSql/9XUfXtq0FNTUWq36iY54h7mgTHBh/7rYE79+OQ+DJN0ub0xZvQ1dGAbc2qAIDbEY/Rqqk9lJWV0MLFDnfuxwEAls8ehN9m78Cbt+9ldCUUm0gkwszfd6JX/2awtDL58g75ePMm91rr6GoAAD58yIFAIICq2qfHLdSEqlBSEuDWjVgAQPVaprh94xEy339ASFAkKhnqQFdfE6eOXoeammqBCQh9Hu+nYuH9LLv4O1TxcJWSErZgwQLMmjUL48ePx5o1a1CrVi1MmzYNR48eRbVq1eDq6orQ0Pz/2i1LTAxz54P99x/sx8/GhnoAAGNDXSS9TJeqz8kRITn1jaRNfv1mZn5AWrr0y4MSX6bB2Ojffo308h43KU1yTAAYPnEtfurgiIh/liArKxvzV/6F3l1dkJGRibCbMTi0dRLu/L0YfuPci3zu5cmOjRegrKyE7j+7fNX+IpEIy+cfgn09C1T7NyGobW+GCupqWL3kKN5nZCEjIwuBi44gJ0eEV//+99Lxx8aoXtMU/brOx9Z1ZzF9Xl+8Ts/AhlUn8eukLli74gR6d5qDscPXIikhraAQ6D94PxUL72fZxd+hpAjkukrJypUrsW7dOrRv3x4A4OrqCmdnZ8THx6Ndu3bQ19fH5MmTcerUqc/2kZmZiczMTKkysTgHAsGXvy6kXPeinqKNu7/kc0U9LUzx6Y7W3f2xyH8AroQ9QK+hi3HpyExcC4/GsTPX5Rht6RQZ8RR7d/yDdTvHfPUDHIsDDuBRdDxWbBohKdOrqIXp8/pi0ez92LfzMpSUBGjVrh5q2lSBklLucVRUleEzuatUXwFTd6Fbbxc8uP8Ml87fwYbdPti58TyWzjuImQv7f/2JlhO8n4qF95OKE3+HFl1ZmQYiS3JNuBMTE2FjYyP5XKNGDaSlpSEpKQmmpqYYOHAgXFwKHo0ICAjA9OnTpcqUdWpDVbf0fE0X/+9fw0YGuohPTJWUGxno4lZELAAgISkNhgbSD/EoKyuhop4WEpJSkZ/4pDQIharQ1dGQ+gvdyEAXCf8eJyExNc/T0Ub//lWekJT/aMrcqf2wYv1xPItPRlMnW0xbsBvvMjJx4uwNuH5vyx8W+bh5/RFSkt+iR/vZkrKcHBECFx3G3u3/YPfxyQXuvzjgAIL+voflG0bAyFhPqq6xcy38ecQXqSlvoaysBG0ddXRpNR2Vq9TLt6/r16LxKCYBE/x6YNXiI/jexRrq6mpo0aYu9g8K+tZTLRd4PxUL72fZxt+hiocJdwmrWbMmTp8+jSFDcp8iPn/+PNTU1GBikvt1XYUKFb44GuHr6wsfHx+pMqPag4sn4K8UG5eIF4kpaNHEDrciHgMAtLXU0ahedazdehoAcPV6FPR1tVDf3hI3bj8CADR3rg0lJQGuhef/dPON2w+RlZWNFk3scPB4CACgRjVTmH1niKvXH/zb7wNMHPUTDCvpIOlV7tdtrZraIy39He49eJqnz+ZNasPaqjKGjl0NAFBWUoLqvw8XqaqWumXbS422PzRAw+9rSJWNG74WbX5wQIcf8y5v9ZFYLMaSOQfxz7k7WLruF1SuUvGzbfX0c5eaCguJRkryWzRpbpunTWbmBywOOIAps3OXrcrJEUMszl0tITs7B6Ic0decXrnD+6lYeD/LNv4OJUUg17vv6+uLvn374syZM6hQoQL279+P0aNHS5LsCxcuwM7OrsA+hEIhhELpdTblMZ1EU0MotSaoRVVD1LE1R0rqGzx5/gor1x/HxNFdEB0bj9i4RPiN64EXiSk4dCp3jnpk9HOcPB+OlXOGYPTk9VBVVcbiGZ7YcygYLxJSAACVjfVxbOdvGOy9CqE3Y5D+OgObdp3H3Cl9kZz6Bq/fZGDR9AG4EhqFkBu5T8if+fsW7j14ivVLRuC32TtgbKgHv3HuWLPlFLKysqXOQShUxWJ/T/QftVyybFVwaCSG9W+DNZtPoUv7xpg4Y2tJXM5S6d27TDyLeyn5/OJZMh7cfwYdXQ0Ym+pDV09Tqr2KijIqVtKGmYWRpGzM0DVo2tIO3Xo1AQAsnn0AZ47fwOwlA6ChKZTM+9TSUoewQu5yV8cOXoN5NSPo6Wvi7q3HWDbvEHr0bSrV70db/jiD712sUdM69yl5+3oWWLXkCDr82BD7dwXBrp6FTK9JWcb7qVh4P8s2/g4tX8rKg46yJNeE293dHdra2ti2bRvevn2LRYsWSUa7AaB79+7o3r27HCMsvAZ1quHU7qmSz/P8PAAAW/dcxNCxq7Fw1WFoqAuxImAw9HQ0EBQaic795iAz84NkH8/RK7B4hieO7fwNIpEYB4+HYKzfJkm9iqoyallVgbq6mqRsgv9WiERi7FzjDaGaCs5cvIVf/7OOqUgkRjfP+Vg6ayAuHPTH23eZ2L73b/gv3JPnHH4b0w0nzt2QjCAAwFi/zdi8fCRO7/HDnwcv48CxEJlcr7Io8u5T/DpkteTzioWHAQDtOjlg8oxeherj+ZNXkiXBAODgnmAAwOjBq6Xa+U53R/sfGwEA4h4n4Y/lx5CelgGTyvroN7gl3Pu65un7YXQ8zp26hQ27P60h27y1PcJDYzBy4CpUNTfE1ICfC3m2io/3U7HwfpZt/B1Kik4gVsB3qaub9ZZ3CCRjsZG8p0REJcGi1k55h0AylhFXuu5ptQaLZNrfw+s+X24kZ6V6TD87OxtxcXHyDoOIiIiIZIQvvill7t69C0tLS3mHQURERET01fjILBERERGVmK9dD78sk2vC3aBBgwLrMzIySigSIiIiIioJXKWkhEVERKBXr16fnTby4sULREVFlXBURERERESyI9eE287ODo6Ojhg+fHi+9eHh4Vi7dm0JR0VERERExaWsPOgoS3JNuJs0aYLIyMjP1mtra8PVNe96pkRERERURnEOd8launRpgfXVq1fH+fPnSygaIiIiIiLZ4yolRERERFRyyt+MktKRcIeEhCA4OBjx8fEAABMTEzg5OaFx48ZyjoyIiIiI6NvINeFOTExEt27dcPnyZZiZmcHY2BgAkJCQAG9vbzRp0gT79u2DkZGRPMMkIiIiIlkph3O45TqoP2LECOTk5ODevXuIjY3F1atXcfXqVcTGxuLevXsQiUTw8vKSZ4hEREREJEsCgWy3MkCuI9wnT57E33//jVq1auWpq1WrFpYtW4bmzZuXfGBERERERDIi14RbKBQiPT39s/WvX7+GUCgswYiIiIiIqFiVw4cm5XrKPXv2RP/+/XHgwAGpxDs9PR0HDhyAp6cnevfuLccIiYiIiEiWxAKBTLeyQK4j3IsWLYJIJEKvXr2QnZ0NNTU1AEBWVhZUVFQwaNAgLFiwQJ4hEhERERF9E7lPKVm1ahXmzp2LsLAwqWUBHRwcoKOjI8/wiIiIiEjWysagtEyVinW4dXR00KJFC3mHQUREREQkc3Kftp6RkYFLly4hIiIiT9379++xZcsWOURFRERERMVCSSDbrQyQa8IdFRUFGxsbuLq6wt7eHs2aNcPz588l9WlpafD09JRjhEREREQkU+VwHW65JtwTJ06EnZ0dEhMTERkZCW1tbbi4uCAuLk6eYRERERERyYxcE+6goCAEBATAwMAAVlZWOHz4MNq2bYumTZvi4cOH8gyNiIiIiIqDQMZbGSDXhDsjIwMqKp+e2xQIBFi1ahU6deqEZs2aISoqSo7REREREZHMlcM53HJdpcTa2hqhoaGwsbGRKl+xYgUAoHPnzvIIi4iIiIhIZuQ6wv3TTz9h586d+datWLECvXv3hlgsLuGoiIiIiKjY8KHJkuXr64tjx459tj4wMBAikagEIyIiIiKiYsU53EREREREJEul4k2TRERERFROlJEHHWWJI9xERERERMWII9xEREREVHLK3wA3E24iIiIiKjniMrKyiCxxSgkRERERlSsrV66EhYUFKlSoAEdHR4SEhBTYfsmSJahVqxbU1dVRtWpVeHt74/3794U+Hke4iYiIiKjkyPmhyV27dsHHxwerV6+Go6MjlixZgrZt2yIyMhJGRkZ52u/YsQOTJk3Chg0b4OzsjKioKAwYMAACgQCLFi0q1DE5wk1EREREJUfO63AvWrQIQ4YMgaenJ2xtbbF69WpoaGhgw4YN+bYPCgpCkyZN8PPPP8PCwgJt2rRB7969vzgq/l9MuImIiIiozMrMzER6errUlpmZmW/brKwshIWFwc3NTVKmpKQENzc3BAcH57uPs7MzwsLCJAn2w4cPcezYMXTo0KHQMXJKCZUJxurW8g6BZOheapS8QyAZalBnu7xDIBnKiJsu7xBI0cn4ocmAgABMny79362fnx+mTZuWp+3Lly+Rk5MDY2NjqXJjY2Pcv38/3/5//vlnvHz5Ei4uLhCLxcjOzsYvv/yCyZMnFzpGjnATERERUclREsh08/X1RVpamtTm6+srs3AvXLiA2bNnIzAwENevX8f+/ftx9OhRzJgxo9B9cISbiIiIiMosoVAIoVBYqLYGBgZQVlZGQkKCVHlCQgJMTEzy3WfKlCno168fBg8eDACwt7fH27dvMXToUPz2229QUvry+DVHuImIiIio5MjxoUk1NTU4ODjg7NmzkjKRSISzZ8/Cyckp333evXuXJ6lWVlYGAIjF4kIdlyPcRERERFRu+Pj4oH///mjYsCEaN26MJUuW4O3bt/D09AQAeHh4oEqVKggICAAAdOrUCYsWLUL9+vXh6OiI6OhoTJkyBZ06dZIk3l/ChJuIiIiISo6c3zTZs2dPJCUlYerUqYiPj0e9evVw4sQJyYOUcXFxUiPav//+OwQCAX7//Xc8e/YMhoaG6NSpE2bNmlXoYwrEhR0LL0PUzXrLOwSSMT41r1i4Soli4SolioU/bxVRTXkHIMWq2zaZ9he9r69M+ysOnMNNRERERFSMOKWEiIiIiEpOORzuZcJNRERERCVHznO45aEc/o1BRERERFRyOMJNRERERCWn/A1wc4SbiIiIiKg4cYSbiIiIiEqMWKn8DXEz4SYiIiKiksOHJkuXJ0+eYODAgfIOg4iIiIjoq5XqhDs5ORmbN2+WdxhEREREJCsCGW9lgFynlBw6dKjA+ocPH5ZQJERERERUIjiHu2R16dIFAoEAYrH4s20E5XCeDxEREREpDrlOKTE1NcX+/fshEony3a5fvy7P8IiIiIhI1gQC2W5lgFwTbgcHB4SFhX22/kuj30RERERUxnAOd8kaP3483r59+9l6KysrnD9/vgQjIiIiIiKSLbkm3E2bNi2wXlNTE82aNSuhaIiIiIio2JXDhyZL9bKARERERERlXalOuGNiYtCyZUt5h0FEREREsqIkkO1WBpTqV7u/efMGFy9elHcYRERERCQj4rKRI8uUXBPuZcuWFVj/7NmzEoqEiIiIiKh4yDXhHjNmDExNTaGmppZvfVZWVglHRERERETFqoxMA5EluSbc5ubmmDt3Ltzd3fOtDw8Ph4ODQwlHRURERETFpoy8rEaW+OIbIiIiIqJiJNcRbn9/f7x79+6z9ba2tnj06FEJRkRERERExYpTSkqWra1tgfWqqqowNzcvoWiIiIiIqNiV6kWpi0epO+U5c+YgNTVV3mEQEREREclEqUu4Z8+ejeTkZHmHQURERETFQSCQ7VYGlLqEmw9JEhEREZEiKXUJd1nVpLE19m4Yh4fXApERtxOd2jTM02aKT3c8DA1EctRmHN0xGdUtTKTq9XU1sXGpFxLurseL2+uwat5QaGoICzyuUKiKxTM88fTmH0i6txE7V4+BkYGuVJuqlSth/8YJeBW5CY+vr8bsyT9DWfnTra9b2wLBxwKQdG8j9m4YB31dTUmdsrISLh+dhYZ1q3/NZSkXtm8/ipYtB8Hevit69BiLW7eiCmx//PgltGv3C+ztu6JTp5G4eDFUql4sFmPp0m1wcfFAnTrdMGDA74iNfS6pz8r6gPHjF6JBA3e0bTsMQUHhUvuvW7cfM2askdn5Kaq9m85i3IAl6NViMvq388Ps8Rvw7HGiVJuszA9YM28f+rWegl7NfTFn4iakvnpdYL9isRg71pyAZ4dpcHediKkjV+N5XJJUm9dp77Bo6jb0bjEZP7f6Dctn7kLGu0xJfcLzZEwethI9m/li8rCVSHgu/a3fTJ91CDp36xuvQNnGn7nlF3/mKoBy+Gr3UpdwR0RElMkHJTU1hLgdEYcxv2/It37s8E4Y4dkOo33Xw7XzFLx9l4nD2yZBKFSVtNm4bCRsan6HH/rMRreB8+HiaI2Vc4YUeNx5U/uho1sD9Bm+FG3c/WFqrI8///CW1CspCbB/0wSoqamgxU9+GOKzCn17NMPUsT0kbQLnDcHFoLtw6uALXW0NTBjZRVI3ZmhHXAmNQujNmK+8Mort2LF/EBCwDl5evXHgwBJYW1ti0KCpePUqNd/216/fw9ix89G9exscPLgUrVp9Dy+vWYiKeixps3btPmzdegTTpo3A7t0LoK5eAYMGTUVmZu6LoHbtOoG7d2Owa9d8uLu3xdixCyTfDD15Eo89e07C27tfsZ97WXf3Rgzad3fGvPWjMW3ZMORkizBt9B94n/Ep8d2w5C9cuxSB8QEemLlqBFJepmPOpE0F9ntg63kc2f0PfpnYHfPW/4oKFdQw/dc/kJX5QdJmsd92xD1MwPTlw/D7wkGIuPEQgQF7JPUblx5CJUMdLN7qA30DbWxadlhSd+n0DQiUBHBuWUd2F6MM4s/c8ok/cxWDWCCQ6VYWlJqEOy4uDlevXkV8fHyZfGjy1IWbmL5gNw6dDM233mtQe8xdfgBHTofhzv04DPYOhKmRPjr/OypTy6oy2raohxET1+JaeAyCrkXCZ+pm9OjsBFNj/Xz71NFWx4CeLTBxxlZcDLqLG7cfYei4NXBqWAuN61sBANxc68CmxncY+OtK3Ip4jFMXbsJ/4R4M82gDVVVlAIC1VRVs2HkO0Y/isfuvINSyqgIAsDAzQv+eLeA3b5esL5fC2LjxINzd26JbNzdYWZlh+vQRqFBBiH37TufbfsuWQ2jatAEGD+6K6tWrYsyYvrC1rY5t244AyB1p2bLlEIYPd4eb2/ewtrbEvHneSExMxpkzVwAAMTFP0LJlY9SoYY4+fToiOTkNKSnpAIBp01Zh3LgB0NLSKJkLUIb5LR2KVj80hlk1E1jWrIzRU3shKT4FMfefAgDevsnAmUMhGPhrZ9RpWANWNlUxakpP3L8Vi8jbj/PtUywW4/Cff8Pd0w2OzexgUaMyfp3WG8kv03H14h0AwJNHCbgefB8jf3NHTTtz2NarhiHjfsKl0+FITkoDADyNTUSLjo1Q2cwQLTs2wtPYBADAm9cZ2L76BIaO71oCV6h048/c8ok/c6msknvCHRgYCHNzc1haWsLZ2Rnff/89jIyM4OLiUuBLccoSCzMjmBrp49ylO5Ky9NcZuBYeA0eHGgAAxwY1kZL2BtdvPZS0OXfpNkQiMRrVy/+rxfr21aCmpiLVb1TMc8Q9TYJjg4/91sCd+3FIfJkmaXP64k3o6mjAtmZVAMDtiMdo1dQeyspKaOFihzv34wAAy2cPwm+zd+DN2/cyuhKKJSvrA+7ejYazc11JmZKSEpyd6+HGjch89wkPvw8np3pSZS4u9REefh8A8PRpApKSUuDs/KmNtrYm6tatiRs3cttYW1siLCwC799n4tKl6zA0rAh9fR0cOnQBQqEqWrd2ku2JlhPv3uT+d66lk/uLM+b+U2Rn56BO45qSNt9ZGMPQRB+Rd2Lz7SPheTJSXr2W2kdTSx01a5tJkvTI27HQ1FaHlU1VSZu6jWpAoCRA1N3cf3uWNUxxMyQKIpEI4VejYGFlCgDYvPww2ndvAsPPJISUiz9zFRN/5ioQJRlvZYBcw1ywYAFmzZqF8ePHY82aNahVqxamTZuGo0ePolq1anB1dUVoaP6jF2WJiWHu/L7//gD++NnYUA8AYGyoi6SX6VL1OTkiJKe+kbTJr9/MzA9IS5d+eVDiyzQYG/3br5Fe3uP+O4pm/G9cwyeuxU8dHBHxzxJkZWVj/sq/0LurCzIyMhF2MwaHtk7Cnb8Xw2+ce5HPXZGlpKQjJ0eESpWkk59KlfTw8mVKvvu8fJkKAwO9fNqnAgCSklIkZZ/rs1u31rC2tkSHDiOwatVuLFkyAWlpb7Bs2XZMmTIMixdvRevWQzFo0FQkJLz69hMtB0QiEdYvPgibOhYwr56b3Ka8eg0VVWVoaatLtdWrqIWUz8zjTn2V/m8bbaly3YraSEnOrUtJfg1dfS2pemUVZWjraEj6HTC6M549TsTQLrPw4kkSBozujLs3YvAo6jladGiIeZO3YNhPs7Bqzl58+JD97RdAwfBnrmLiz1wFUg7ncMv1xTcrV67EunXr0L59ewCAq6srnJ2dER8fj3bt2kFfXx+TJ0/GqVOnPttHZmYmMjMzpcrE4hwIBMrFGrsiuRf1FG3c/SWfK+ppYYpPd7Tu7o9F/gNwJewBeg1djEtHZuJaeDSOnbkux2hJVVUFfn7Dpcp8fZegX79OiIh4iLNnr+Cvv5Zh3bp9mDlzDZYvnyynSMuOP+bvx+OH8QhYM1LeoQAAKhnp4vdFgyWfP2RlY/qvf2D01F7Ys/E01DWEWLlnEqb/+gdOHgjGD+5N5RgtFRV/5pYt/JlLsiDXEe7ExETY2NhIPteoUQNpaWlISsp9on/gwIEIDg4usI+AgADo6upKbdnpEcUad1HF/zu68f9PshsZ6CIhKRUAkJCUBkMDHal6ZWUlVNTTkrTJr1+hUBW6OtJzx4wMdJGQ+G+/ial5j/vvKEtCkvQozEdzp/bDivXH8Sw+GU2dbLH/6BW8y8jEibM34Pp9wW8HLU/09XWgrKyEV6+kR1ZevUqFgUH+X/kbGHwaWZFurwcAMDTUl5QVts8rV27hwYM49O3bESEht+Hq2hAaGhXQvr0LQkLu5LsPffLH/P24dikCMwOHw8BYT1KuX0kb2R9y8OZ1hlT71OQ30K+kjfzoVdL5t430CHha8mvoV8yt06+ojbSUN1L1Odk5eJ3+7rP97t10BvUca8LKpiruXI+BU4s6UFFRhlNze9wJ48N1/48/cxUTf+YqEK7DXbJq1qyJ06c/Pehw/vx5qKmpwcQkd+mmChUqQPCFC+nr64u0tDSpTUWndP2Aio1LxIvEFLRoYicp09ZSR6N61XE17AEA4Or1KOjraqG+vaWkTXPn2lBSEuBaeP6/UG/cfoisrGypfmtUM4XZd4a4ev1jvw9gZ20Gw0qffrG0amqPtPR3uPfgaZ4+mzepDWuryli1KfdbBWUlJaiq5H5boKqqIrW0VXmnpqaK2rWtEBz8aXk2kUiE4OCbqF+/Vr771KtnjStXbkqVBQWFo149awDAd98Zw9BQH8HBn9q8efMON29GoX596zz9ZWZmwd9/Nfz9vaCsrIycHBGys3OnGGRn5yAnR/TN56moxGIx/pi/H1cu3saMlcNhXLmSVH116++goqKMW9ceSMqePU5EUnwKatlZ5NunceWK0K+kLbXPuzfvEXU3DrXsc1dfqmVvgbevMxB974mkza3QaIhFYtSsbZanzyePEvD3yRv4eVg7AIAoR4Sc7BwAQHa2CCIR7/H/489cxcSfuVSWyfVfsq+vL7y8vNCzZ0/0798fP/74I0aPHi1Jsi9cuAA7O7sC+xAKhdDR0ZHa5DGdRFNDiDq25qhjm/tL1aKqIerYmqPqv7/EV64/jomju6BjawfUrlUV6xcPx4vEFBw6lTtHPTL6OU6eD8fKOUPQsG51ODWsicUzPLHnUDBeJOT+NV/ZWB/h5xZI1mdNf52BTbvOY+6UvnB1skV9e0v8seAXXAmNQsiNaADAmb9v4d6Dp1i/ZATsbczg5loHfuPcsWbLKWRlSc/9FApVsdjfE16T1kmWPAoOjcSw/m1gb2OGLu0bIzg0/wdTyitPzy7YvfskDhw4i5iYJ5g2LRAZGe/RtasbAGDChEVYuHCzpL2HR2f88891bNhwADExT7B8+Q7cuRONvn1/AAAIBAJ4eHTGqlW7cPbsVURGxmLChEUwMqoIN7fv8xw/MPBPNGvmAFvb3P8mGjSwwenTwbh//xG2bTuCBg1s8uxDudbM348LJ8Lg498X6ppCpLxKR8qrdGS+z12+T1NLHW6dG2Pj0kO4HRqN6HtPsGzGn6hlby5JngHAy30Orly4DSD3/nXq5Yo9G88g5O87iI1+gSXTd6CigQ4cm+X+LKtqaYwGTtYIDNiDqLtxuHfzEdYu2A+X1vVQ0VB6ZFQsFiMwYA8Gev+ICuq560Nb17XE6b+u4MmjBFw4HgqbOpYoj/gzt3ziz1wFUQ7ncAvEcn614/Hjx7Ft2zZkZmaibdu2GDLk0xqor17lPnxQqVKlz+2eL3Wz3jKNsTCafm+DU7un5infuuciho5dDSD3JQwDf24FPR0NBIVG4tffNiD6Ubykrb6uJhbP8EQHtwYQicQ4eDwEY/024e2/L8Qw+84AkUHL0cbdH/9cuQcg9wf2nN/7wv1HZwjVVHDm4i38+vsGqa8uzaoYYOmsgXB1ssXbd5nYvvdv/D5nZ56/xP0n9oKqijJ8Z22XlFUzN8bm5SNRo1pl/HnwMrynbJTL20Az4qaX+DELa9u2I1i/fj+SklJgY1MNv/8+FHXr5o629OvniypVjDBnzqd1eo8fv4QlS7bh2bMEWFhUxvjxnmjW7NNLO8RiMZYt247du08iPf0tHBxs4ec3HJaWVaSOGxX1GCNHzsLBg8ugoVEBQO5oj7//ahw+fBGWllWwcOE4mJtXLoGrUDT3Ugt+UUVJ6OI4Nt/yUVN6otUPjQHkvvhm49JD+Of0DXzIykH972th2ISu0P/P6GUXx7FS+4jFYuz84yROHbyCt28yYFPXEsMmdEMVM0PJPq/T3uGPBblTWZQEAji1sMfgsT9B/f9eunJyfzDCQ6IwcU5/SVlq8mssmrodD+7Gob6TNX6d2gvCCmoyuy5fo0Gd7V9uJGP8mVt8SvPPW4A/c79OzS83KUGWE4/ItL9Hc3+QaX/FQe4Jd3GQR8JNxau0/wKgoikNCTfJjjwSbio+/HmriJhwy1upnhyWnZ2NuLg4eYdBRERERDIiVhLIdCsLSnXCfffuXVhals/5iUREREQKqRzO4S7VCTcRERERUVkn1xffNGjQoMD6jIyMAuuJiIiIqIwpI2tny5JcE+6IiAj06tXrs9NGXrx4gagoPlxFREREpDDK4fwKuSbcdnZ2cHR0xPDhw/OtDw8Px9q1a0s4KiIiIiIi2ZFrwt2kSRNERn5+UX9tbW24urqWYEREREREVKw4paRkLV26tMD66tWr4/z58yUUDRERERGR7Mk14SYiIiKicqaMLOUnS6Ui4Q4JCUFwcDDi43NfuWtiYgInJyc0btxYzpERERERkUwx4S5ZiYmJ6NatGy5fvgwzMzMYGxsDABISEuDt7Y0mTZpg3759MDIykmeYRERERERfTa4Ls4wYMQI5OTm4d+8eYmNjcfXqVVy9ehWxsbG4d+8eRCIRvLy85BkiEREREcmQWCCQ6VYWyHWE++TJk/j7779Rq1atPHW1atXCsmXL0Lx585IPjIiIiIiKRzlch1uupywUCpGenv7Z+tevX0MoFJZgREREREREsiXXhLtnz57o378/Dhw4IJV4p6en48CBA/D09ETv3r3lGCERERERyZRAINutDJDrlJJFixZBJBKhV69eyM7OhpqaGgAgKysLKioqGDRoEBYsWCDPEImIiIiIvolcE26hUIhVq1Zh7ty5CAsLk1oW0MHBATo6OvIMj4iIiIhkjcsCyoeOjg5atGgh7zCIiIiIqLiVw4Rb7s+JZmRk4NKlS4iIiMhT9/79e2zZskUOURERERERyYZcE+6oqCjY2NjA1dUV9vb2aNasGZ4/fy6pT0tLg6enpxwjJCIiIiKZEsh4KwPkmnBPnDgRdnZ2SExMRGRkJLS1teHi4oK4uDh5hkVERERExUSsJJDpVhbINeEOCgpCQEAADAwMYGVlhcOHD6Nt27Zo2rQpHj58KM/QiIiIiIhkQq4Jd0ZGBlRUPj23KRAIsGrVKnTq1AnNmjVDVFSUHKMjIiIiIpnjOtwly9raGqGhobCxsZEqX7FiBQCgc+fO8giLiIiIiIpLGZkGIktyHeH+6aefsHPnznzrVqxYgd69e0MsFpdwVEREREREsiPXhNvX1xfHjh37bH1gYCBEIlEJRkRERERExYqrlBARERERkSyVijdNEhEREVH5oFQOh3uZcBMRERFRiSkjC4vIVDn8G4OIiIiIqOR81Qj3gwcPcP78eSQmJuZ5qHHq1KkyCYyIiIiIFA9HuAth7dq1sLGxwdSpU7F3714cOHBAsh08eLAYQiQiIiIiRSEQCGS6fY2VK1fCwsICFSpUgKOjI0JCQgpsn5qaCi8vL5iamkIoFKJmzZoFrrT3/4o8wj1z5kzMmjULEydOLOquRERERERytWvXLvj4+GD16tVwdHTEkiVL0LZtW0RGRsLIyChP+6ysLLRu3RpGRkbYu3cvqlSpgsePH0NPT6/Qxyxywp2SkoIePXoUdTciIiIiIrlPKVm0aBGGDBkCT09PAMDq1atx9OhRbNiwAZMmTcrTfsOGDUhOTkZQUBBUVVUBABYWFkU6ZpGnlPTo0QOnTp0q6m5ERERERBAIZLtlZmYiPT1dasvMzMz32FlZWQgLC4Obm5ukTElJCW5ubggODs53n0OHDsHJyQleXl4wNjaGnZ0dZs+ejZycnEKfc6FGuJctWyb5/1ZWVpgyZQquXLkCe3t7Sab/0ejRowt9cKLCSs68L+8QSIYa1Nkp7xBIhjLipss7BCIqxwICAjB9uvTPIT8/P0ybNi1P25cvXyInJwfGxsZS5cbGxrh/P/9c4+HDhzh37hz69OmDY8eOITo6GiNGjMCHDx/g5+dXqBgLlXAvXrxY6rOWlhYuXryIixcvSpULBAIm3ERERET0WQIZL0rt6+sLHx8fqTKhUCiz/kUiEYyMjPDHH39AWVkZDg4OePbsGebPny/bhPvRo0ffFCgRERERUXEQCoWFTrANDAygrKyMhIQEqfKEhASYmJjku4+pqSlUVVWhrKwsKbOxsUF8fDyysrKgpqb2xeMW+W8Mf39/vHv3Lk95RkYG/P39i9odEREREZUjsp7DXRRqampwcHDA2bNnJWUikQhnz56Fk5NTvvs0adIE0dHRUu+eiYqKgqmpaaGSbeArEu7p06fjzZs3ecrfvXuXZ/4MEREREdF/KQlkuxWVj48P1q5di82bN+PevXsYPnw43r59K1m1xMPDA76+vpL2w4cPR3JyMn799VdERUXh6NGjmD17Nry8vAp9zCIvCygWi/NdZPzmzZuoWLFiUbsjIiIiIioxPXv2RFJSEqZOnYr4+HjUq1cPJ06ckDxIGRcXByWlT2PSVatWxcmTJ+Ht7Y06deqgSpUq+PXXX4v0TppCJ9z6+vqSN/rUrFlTKunOycnBmzdv8MsvvxT6wERERERU/sh7HW4AGDlyJEaOHJlv3YULF/KUOTk54cqVK199vEIn3EuWLIFYLMbAgQMxffp06OrqSurU1NRgYWHx2bkvRERERERA6Ui4S1qhE+7+/fsDACwtLeHs7Jxn/W0iIiIiIsqryHO4mzVrBpFIhKioKCQmJko9sQkArq6uMguOiIiIiBRLfs8CKroiJ9xXrlzBzz//jMePH0MsFkvVCQSCIr3mkoiIiIhI0RU54f7ll1/QsGFDHD16FKampuXyrxQiIiIi+jqyftNkWVDkhPvBgwfYu3cvrKysiiMeIiIiIlJg5XGstsh/Yzg6OiI6Oro4YiEiIiIiUjhFHuEeNWoUxo4di/j4eNjb2+dZraROnTpF6i8jIwNhYWGoWLEibG1tperev3+P3bt3w8PDo6hhEhEREVEpVB5HuIuccHfr1g0AMHDgQEmZQCCQvIGyKA9NRkVFoU2bNoiLi4NAIICLiwv+/PNPmJqaAgDS0tLg6enJhJuIiIhIQTDhLoRHjx7J7OATJ06EnZ0dQkNDkZqaijFjxqBJkya4cOECzMzMZHYcIiIiIiJ5KXLCbW5uLrODBwUF4cyZMzAwMICBgQEOHz6MESNGoGnTpjh//jw0NTVldiwiIiIikj+lcjjC/VULs8TExGDUqFFwc3ODm5sbRo8ejZiYmCL3k5GRARWVTzm/QCDAqlWr0KlTJzRr1gxRUVFfEx4RERERlVICgWy3sqDICffJkydha2uLkJAQ1KlTB3Xq1MHVq1dRu3ZtnD59ukh9WVtbIzQ0NE/5ihUr8OOPP6Jz585FDY+IiIiIqFQp8pSSSZMmwdvbG3PmzMlTPnHiRLRu3brQff3000/YuXMn+vXrl6duxYoVEIlEWL16dVFDJCIiIqJSqqyMSsuSQPz/72f/ggoVKuD27duoUaOGVHlUVBTq1KmD9+/fyzTAr6Fu1lveIZCMPXvAe6pIqtTYKe8QSIYy4qbLOwQiKlBNeQcgpeGf/8i0v9BeTWXaX3Eo8pQSQ0NDhIeH5ykPDw+HkZGRLGIiIiIiIgUlUBLIdCsLijylZMiQIRg6dCgePnwIZ2dnAMDly5cxd+5c+Pj4yDS4mJgYDBkyBOfOnZNpv0REREQkH+VxSkmRE+4pU6ZAW1sbCxcuhK+vLwCgcuXKmDZtGkaPHi3T4N68eYOLFy/KtE8iIiIiopJU5IRbIBDA29sb3t7eeP36NQBAW1v7qw6+bNmyAuufPXv2Vf0SERERUenEEe4i+tpE+6MxY8bA1NQUampq+dZnZWV9U/9EREREVLow4S5Ay5YtC9WuKPOtzc3NMXfuXLi7u+dbHx4eDgcHh0L3R0RERERU2hQ64b5w4QLMzc3RsWNHqKqqyuTgDg4OCAsL+2zCLRAIUMRVC4mIiIioFCsjC4vIVKET7rlz52Ljxo3Ys2cP+vTpg4EDB8LOzu6bDu7v74937959tt7W1haPHj36pmMQERERUelRHqeUFHod7vHjxyMiIgIHDx7E69ev0aRJEzRu3BirV69Genr6Vx3c1tYWDRs2/Gy9qqoqzM3Nv6pvIiIiIqLSoMgvvnFycsLatWvx4sULeHl5YcOGDahcufJXJ93/b86cOUhNTZVJX0RERERUugiUZLuVBV8d5vXr13Hx4kXcu3cPdnZ2MpvXPXv2bCQnJ8ukLyIiIiIieStSwv38+XPMnj0bNWvWRPfu3VGxYkVcvXoVV65cgbq6ukwC4kOSRERERIpLIJDtVhYU+qHJDh064Pz582jTpg3mz5+Pjh07QkXlm5bxJiIiIqJyRlBWsmQZKvQI94kTJ1CxYkXExcVh+vTpaNy4MRo0aJBn+1YRERFl8kHJJo2tsXfDODy8FoiMuJ3o1Cbvw6BTfLrjYWggkqM24+iOyahuYSJVr6+riY1LvZBwdz1e3F6HVfOGQlNDWOBxhUJVLJ7hiac3/0DSvY3YuXoMjAx0pdpUrVwJ+zdOwKvITXh8fTVmT/4Zysqfbn3d2hYIPhaApHsbsXfDOOjrakrqlJWVcPnoLDSsW/1rLotCuRH6EONGbkCnVjPgVGc8Lp67I6nL/pCDlYuPok/XhWjReDI6tZqB6ZN3IikxrcA+1wWeglOd8VJbz87zpNpkZn7A/Fn70bapH1o6/gZf781IfvVaUp+W9g7jRm5AS8ff4OG+GJH3pN/QOn/WfuzYfFEGV6Bs47/R8mn79qNo2XIQ7O27okePsbh1K6rA9sePX0K7dr/A3r4rOnUaiYsXQ6XqxWIxli7dBhcXD9Sp0w0DBvyO2NjnkvqsrA8YP34hGjRwR9u2wxAUFC61/7p1+zFjxhqZnV95xHtKZVGhE24/Pz8MHToUXbp0wY8//vjZ7WvFxcXh6tWriI+PL5MPTWpqCHE7Ig5jft+Qb/3Y4Z0wwrMdRvuuh2vnKXj7LhOHt02CUPhp7vvGZSNhU/M7/NBnNroNnA8XR2usnDOkwOPOm9oPHd0aoM/wpWjj7g9TY338+Ye3pF5JSYD9myZATU0FLX7ywxCfVejboxmmju0haRM4bwguBt2FUwdf6GprYMLILpK6MUM74kpoFEJvxnzllVEc7zOyUKNWZYyd3CVv3fssRN57Bs9hbti0awwCFnkgLjYJE0Zv+mK/1aob48i5KZJtzWYvqfql8w7h8sV7mLWgHwI3DsfLpHRM8t4sqd+89izevcvEpl1j0KBhdcyZvldSd+fmY0TcfoKefZt+9XkrCv4bLX+OHfsHAQHr4OXVGwcOLIG1tSUGDZqKV69S821//fo9jB07H927t8HBg0vRqtX38PKahaiox5I2a9fuw9atRzBt2gjs3r0A6uoVMGjQVGRm5r4ZedeuE7h7Nwa7ds2Hu3tbjB27QDJV8smTeOzZcxLe3v2K/dwVFe+pYuCUkgL4+fkVSwCBgYGYO3cunj59KlXu5OSEpUuXlpk3TZ66cBOnLtz8bL3XoPaYu/wAjpwOAwAM9g7E47DV6NymIfYcDkYtq8po26IemvzwG67feggA8Jm6GQc3T4DvrO14kZCSp08dbXUM6NkCA0Yvx8WguwCAoePW4Ob5hWhc3wohN6Lh5loHNjW+Q8efZyPxZRpuRTyG/8I9mDmpN2Yu3osPH3JgbVUFnqNXIvpRPHb/FYT2rXK/qbAwM0L/ni3g3HGyrC9XmeTU1BpOTa3zrdPSVseyP4ZKlY2d/BMG/bwM8S9SYGKq/9l+lVWUUMlAJ9+6N68zcPjANUyf8zMaOloBAH6b0RO9f5yPOzcfw66uOWIfJsKtXT2YWRiiS3dH/LX3CoDcUfd5M/fBd1oPqdHS8or/RsufjRsPwt29Lbp1cwMATJ8+AhcuXMO+facxdGiPPO23bDmEpk0bYPDgrgCAMWP6IigoHNu2HYG/vxfEYjG2bDmE4cPd4eb2PQBg3jxvODv3w5kzV9CxoytiYp6gZcvGqFHDHFWrmmDevI1ISUlHxYq6mDZtFcaNGwAtLY2SuwgKhvdUMZSVJFmW5PpbeMGCBZg1axbGjx+PNWvWoFatWpg2bRqOHj2KatWqwdXVFaGhoV/uqJSzMDOCqZE+zl36NAUh/XUGroXHwNGhBgDAsUFNpKS9kfwiB4Bzl25DJBKjUb38vyqub18NamoqUv1GxTxH3NMkODb42G8N3Lkfh8SXn6Y2nL54E7o6GrCtWRUAcDviMVo1tYeyshJauNjhzv04AMDy2YPw2+wdePP2vYyuRPny5k0GBAIBtLULfqD4yeOX6NRqBrq1D4DfpB2If/Epcbsf8QzZ2Tlo9H0NSZmFpRFMTPVw+1buCI1VLVOEhUQjOzsHVy5HwaqmKQBg28YLqN+wOmxqVy2Gs1Ms/DeqeLKyPuDu3Wg4O9eVlCkpKcHZuR5u3IjMd5/w8PtwcqonVebiUh/h4fcBAE+fJiApKQXOzp/aaGtrom7dmrhxI7eNtbUlwsIi8P59Ji5dug5Dw4rQ19fBoUMXIBSqonVrJ9meaDnCe0plmVyfely5ciXWrVuH9u3bAwBcXV3h7OyM+Ph4tGvXDvr6+pg8eTJOnTolzzC/mYlh7nzN//5C/fjZ2FAPAGBsqIukl9JrmefkiJCc+kbSJr9+MzM/IC1d+m2diS/TYGz0b79GenmPm5QmOSYADJ+4FktnDsSYoR0RHBqF+Sv/Qu+uLsjIyETYzRgc2joJ1cyNsedQMKYv2F3k8y+PMjM/IHDxMbRuXw+aWhU+2662vRl+n9kT5haGeJn0GutXn8bwAYHYtn8sNDUr4NXL11BVVYa2jnTSrl9JG8kvc+dxewxsgXkz96NHxzkwqVwRk6e748njJBw7HIq1W0di7ox9CAmKgnXt7+Dr1x1aX/gDoDziv1HFk5KSjpwcESpVkv52qVIlPTx8+DTffV6+TIWBgV6e9i9fpgIAkpJSJGV52+TWdevWGpGRsejQYQT09XWwZMkEpKW9wbJl27F162wsXrwVx479AzMzE8ye/SuMjSt9+8mWE7yniqM8jnDLNeFOTEyEjY2N5HONGjWQlpaGpKQkmJqaYuDAgXBxcSmwj8zMTGRmZkqVicU5EAiUiyVmRXQv6inauPtLPlfU08IUn+5o3d0fi/wH4ErYA/QauhiXjszEtfBoHDtzXY7Rln7ZH3Lw+7htEIuBCb93LbDtf6eoWNXMTcB/ajcbZ0/eQueujQt1PC1tdfjP7SNVNnLQaoz06YiTR2/g+dNX2HVoAgKm78GGNWcwelynop8UyRX/jZYdqqoq8PMbLlXm67sE/fp1QkTEQ5w9ewV//bUM69btw8yZa7B8OacDlXa8pyQLcp1SUrNmTZw+fVry+fz581BTU4OJSe7KABUqVPji0jEBAQHQ1dWV2rLTI4o17qKK/3e06v9XJjAy0EVCUioAICEpDYb/N49XWVkJFfW0JG3y61coVIWujvTcMSMDXSQk/ttvYmre4/47apaQlP8KGnOn9sOK9cfxLD4ZTZ1ssf/oFbzLyMSJszfg+r3tF8+3PMv+kIPfxm9F/IsULPtjSIGj2/nR1lGHmbkBnj55CQCoZKCNDx9y8Do9Q6pdyqvXqGignW8fRw5eg5aOOlxb2OF6aAxcW9hBRVUZLdvUxfVrfLAuP/w3qnj09XWgrKyEV6+k59a/epUKA4P8n6kwMPg08indXg8AYGioLykrbJ9XrtzCgwdx6Nu3I0JCbsPVtSE0NCqgfXsXhITcyXcfyh/vqeJQEsh2KwuKnHBv2bIlz4gyAGRlZWHLli1F6svX1xdeXl7o2bMn+vfvjx9//BGjR4+WJNkXLlyAnZ3dF/tIS0uT2lR0StcvnNi4RLxITEGLJp/ORVtLHY3qVcfVsAcAgKvXo6Cvq4X69paSNs2da0NJSYBr4fknSTduP0RWVrZUvzWqmcLsO0Ncvf6x3wewszaDYaVPiUKrpvZIS3+Hew/yfgXXvEltWFtVxqpNudN4lJWUoKqS+22BqqoKH74rwMdk++njl1j2x1Do6ml+eaf/8+5dJp4+eQWDfxM7a9sqUFFRRujVB5I2jx8lIv5FKuzr5F0+MyX5DTasPg2fSV0AAKIcEbKzc3Ljy86BSCT6ijNTfPw3qnjU1FRRu7YVgoNvScpEIhGCg2+ifv1a+e5Tr541rlyRfrA2KCgc9erlfhP13XfGMDTUR3DwpzZv3rzDzZtRqF8/7wPVmZlZ8PdfDX9/LygrKyMnR4Ts7GwAuf8ec3L477EoeE8VBxPuQvD09ERaWt5Rl9evX8PT07NIfbm7u+Ovv/6CiooK3r59i0WLFiEgIEBS3717dxw+fLjAPoRCIXR0dKQ2eUwn0dQQoo6tOerY5iZBFlUNUcfWHFUr587lWrn+OCaO7oKOrR1Qu1ZVrF88HC8SU3DoVO5DoZHRz3HyfDhWzhmChnWrw6lhTSye4Yk9h4Ilqx9UNtZH+LkFkvV2019nYNOu85g7pS9cnWxR394Sfyz4BVdCoxByIxoAcObvW7j34CnWLxkBexszuLnWgd84d6zZcgpZWdlS5yAUqmKxvye8Jq2TLHkUHBqJYf3bwN7GDF3aN0ZwaP4PppQH795lIur+M0Tdz13n+vmzZETdf4b4FynI/pCDyWO34P7dp5g252eIRCK8epmOVy/T8eHDp+s8cvAa7Nl5WfJ52YLDuB4agxfPknErPBaTxmyGsrISWrevByB3ukinnxph2YLDCAuJxv2Ip5g5dTfs6prDrm7ehHvJvEPo3b8ZjIxzR0jr1LPAiSPXEfswAX/tvQr7ehbFd4FKOf4bLX88Pbtg9+6TOHDgLGJinmDatEBkZLxH1665K1xMmLAICxd+WmLTw6Mz/vnnOjZsOICYmCdYvnwH7tyJRt++PwDIfVmHh0dnrFq1C2fPXkVkZCwmTFgEI6OKkhUu/isw8E80a+YAW9vc/x4aNLDB6dPBuH//EbZtO4IGDWzy7EMF4z2lsqrIc7jFYnG+0zyePn0KXV3dfPYoWPv27SUPTf6/SpXKzoMHDepUw6ndUyWf5/l5AAC27rmIoWNXY+Gqw9BQF2JFwGDo6WggKDQSnfvNQWbmB8k+nqNXYPEMTxzb+RtEIjEOHg/BWL9NknoVVWXUsqoCdXU1SdkE/60QicTYucYbQjUVnLl4C7/+Z51hkUiMbp7zsXTWQFw46I+37zKxfe/f8F+4J885/DamG06cu4FbEZ/WJx3rtxmbl4/E6T1++PPgZRw4FiKT61UW3b/7FF6DVks+L5uf+8dgh84OGDy8Df65kDuVyaPHYqn9Vq7/BQ0a5f5wfvb0FdJS3krqkhLT4DdxB9JS30JPXwt1G1hg7baR0K+oJWnz64TOECgJ4OuzBR+ysuHYpBbG//ZTnviuXI7E07iX8JvdS1LWvXcT3It4ikF9lsPWrioG/dJGBleibOK/0fKnQ4emSE5Ow7Jl25GUlAIbm2pYt266ZKrAixdJUPrP8FiDBjZYsGAclizZhkWLtsDCojJWrvwNNWt++uN2yJBuyMh4j6lTVyA9/S0cHGyxbt10CIVqUseOinqM48cv4eDBZZKydu2aICTkNvr0mQRLyypYuHBcMV8BxcN7qhiUBGJ5h1DiBOKPwyRfUL9+fQgEAty8eRO1a9eWeq17Tk4OHj16hHbt2mH3btk9IZ+dnY3nz5/DzMysSPupm/WWWQxUOjx7wHuqSKrU2CnvEEiGMuKmyzsEIipQTXkHIKX9qUsy7e94m4IX2CgNCj3C3aVLFwBAeHg42rZtCy2tTyNwampqsLCwQLdu3WQa3N27d9GgQQPk5OTItF8iIiIiopJSpDdN5uTkwMLCAm3atIGpqWlxxkVERERECqg8PtpdpDncysrKGDZsGO7duyeTgzdo0KDA+oyMjALriYiIiKhsKY9zuIv80KSdnR0ePnwIS0vLLzf+goiICPTq1euzfb148QJRUVHffBwiIiIiInkpcsI9c+ZMjBs3DjNmzICDgwM0NaXXGtbR0fnMnnnZ2dnB0dERw4cPz7c+PDwca9euLWqIRERERFRKlZW1s2WpyAl3hw4dAACdO3eWWh7w43KBRXnAsUmTJoiM/Pyasdra2nB1dS1qiEREREREpUaRE+7z58/L7OBLly4tsL569eoyPR4RERERyRcfmiyEZs2aFUccRERERFQOcEpJIaWmpmL9+vWS1Upq166NgQMHftWbJgEgJCQEwcHBiI+PBwCYmJjAyckJjRs3/qr+iIiIiIhKiyIn3KGhoWjbti3U1dUlCfGiRYswa9YsnDp16otL/f1XYmIiunXrhsuXL8PMzAzGxsYAgISEBHh7e6NJkybYt28fjIyMihomEREREZVCAi4L+GXe3t7o3Lkz1q5dK3m9e3Z2NgYPHowxY8bg77//LnRfI0aMQE5ODu7du4datWpJ1UVGRmLgwIHw8vLCnj17ihomEREREZVCnFJSCKGhoVLJNgCoqKhgwoQJaNiwYZH6OnnyJP7+++88yTYA1KpVC8uWLUPz5s2LGiIRERERUalR5AdFdXR0EBcXl6f8yZMn0NbWLlJfQqEQ6enpn61//fo1hEJhUUMkIiIiolJKScZbWVDkOHv27IlBgwZh165dePLkCZ48eYI///wTgwcPRu/evYvcV//+/XHgwAGpxDs9PR0HDhyAp6dnkfskIiIiotJLSSCW6VYWFHlKyYIFCyAQCODh4YHs7GwAgKqqKoYPH445c+YUqa9FixZBJBKhV69eyM7OhpqaGgAgKysLKioqGDRoEBYsWFDUEImIiIiISg2BWCz+qj8N3r17h5iYGAC5L6jR0ND46iDS09MRFhYmtSygg4NDkV4T/1/qZhwVVzTPHvCeKpIqNXbKOwSSoYy46fIOgYgKVFPeAUjpe/GiTPvbVgbeEfNV63ADgIaGBuzt7WUShI6ODlq0aCGTvoiIiIiISpMiJ9xv377FnDlzcPbsWSQmJkIkEknVP3z4sEj9ZWRkICwsDBUrVoStra1U3fv377F79254eHgUNUwiIiIiKoXKyoOOslTkhHvw4MG4ePEi+vXrB1NTUwgEX7+YYlRUFNq0aYO4uDgIBAK4uLhg586dqFy5MgAgLS0Nnp6eTLiJiIiIFATX4S6E48eP4+jRo2jSpMk3H3zixImws7NDaGgoUlNTMWbMGLi4uODChQswMzP75v6JiIiIiOStyAm3vr4+KlasKJODBwUF4cyZMzAwMICBgQEOHz6MESNGoGnTpjh//jw0NTVlchwiIiIiKh3KylJ+slTkaTQzZszA1KlT8e7du28+eEZGhtQbKwUCAVatWoVOnTqhWbNmiIqK+uZjEBEREVHpoSSQ7VYWFGqEu379+lJztaOjo2FsbAwLCwuoqqpKtb1+/XqhD25tbY3Q0FDY2NhIla9YsQIA0Llz50L3RURERERUGhUq4e7SpUuxHPynn37Czp070a9fvzx1K1asgEgkwurVq4vl2ERERERU8srjKiVf/eKb0owvvlE8fPGNYuGLbxQLX3xDVNqVrhffDL10Qab9/eHSXKb9FYevfvFNWFgY7t27BwCoXbs26tevL7OgiIiIiEgxlceHJouccCcmJqJXr164cOEC9PT0AACpqalo0aIF/vzzTxgaGso6RiIiIiJSEGXlQUdZKvI0mlGjRuH169e4e/cukpOTkZycjDt37iA9PR2jR48ujhiJiIiIiMqsIo9wnzhxAmfOnJFaWcTW1hYrV65EmzZtZBocERERESmW8jjCXeSEWyQS5VkKEABUVVUhEolkEhQRERERKabyuEpJkc+5ZcuW+PXXX/H8+XNJ2bNnz+Dt7Y1WrVrJNDgiIiIiIllbuXIlLCwsUKFCBTg6OiIkJKRQ+/35558QCARFXjK7yAn3ihUrkJ6eDgsLC1SvXh3Vq1eHpaUl0tPTsXz58qJ2R0RERETliJJALNOtqHbt2gUfHx/4+fnh+vXrqFu3Ltq2bYvExMQC94uNjcW4cePQtGnTIh+zyFNKqlatiuvXr+PMmTO4f/8+AMDGxgZubm5FPjgRERERlS/ynsO9aNEiDBkyBJ6engCA1atX4+jRo9iwYQMmTZqU7z45OTno06cPpk+fjn/++QepqalFOuZXrcMtEAjQunVrtG7d+mt2JyIiIiIqcVlZWQgLC4Ovr6+kTElJCW5ubggODv7sfv7+/jAyMsKgQYPwzz//FPm4hZ5Scu7cOdja2iI9PT1PXVpaGmrXrv1VARARERFR+aEk4y0zMxPp6elSW2ZmZr7HfvnyJXJycmBsbCxVbmxsjPj4+Hz3uXTpEtavX4+1a9d+9TkXeoR7yZIlGDJkCHR0dPLU6erqYtiwYVi0aNFXzWsh+hK+Clyx8FXgREQkKwEBAZg+Xfr3ip+fH6ZNm/bNfb9+/Rr9+vXD2rVrYWBg8NX9FDrhvnnzJubOnfvZ+jZt2mDBggVfHQgRERERKT5Zz+H29fWFj4+PVJlQKMy3rYGBAZSVlZGQkCBVnpCQABMTkzztY2JiEBsbi06dOknKPi6DraKigsjISFSvXv2LMRY64U5ISMh3/W1JRyoqSEpKKmx3RERERFQOCb5iZZGCCIXCzybY/09NTQ0ODg44e/asZGk/kUiEs2fPYuTIkXnaW1tb4/bt21Jlv//+O16/fo2lS5eiatWqhTpuoRPuKlWq4M6dO7Cyssq3/tatWzA1NS1sd0REREREJc7Hxwf9+/dHw4YN0bhxYyxZsgRv376VrFri4eGBKlWqICAgABUqVICdnZ3U/np6egCQp7wghU64O3TogClTpqBdu3aoUKGCVF1GRgb8/Pzwww8/FPrARERERFT+yHtZwJ49eyIpKQlTp05FfHw86tWrhxMnTkgepIyLi4OSkmzfhykQi8WFGtdPSEhAgwYNoKysjJEjR6JWrVoAgPv372PlypXIycnB9evX8zz1KQ/qZr3lHQIRFYAPTRIRlaSa8g5Aym+hZ2Xa36yGpf9N54Ue4TY2NkZQUBCGDx8OX19ffMzTBQIB2rZti5UrV5aKZJuIiIiIqDQp0otvzM3NcezYMaSkpCA6OhpisRg1atSAvr5+ccVHRERERArka17HXtZ91Zsm9fX10ahRI1nHQkREREQKTt5zuOVBtjPCiYiIiIhIyleNcBMRERERfQ2OcBMRERERkUxxhJuIiIiISoyyvAOQAybcRERERFRiyuMqJaV6Ssnbt2/x999/yzsMIiIiIqKvVqpHuKOjo9GiRQvk5OTIOxQiIiIikoHy+NBkqU64iYiIiEixMOEuYRUrViywniPbRERERFTWyTXhzszMxPDhw2Fvb59v/ePHjzF9+vQSjoqIiIiIiosyR7hLVr169VC1alX0798/3/qbN28y4SYiIiKiMk2uCXfHjh2Rmpr62fqKFSvCw8Oj5AIiIiIiomLFOdwlbPLkyQXWV61aFRs3biyhaIiIiIiouHEdbiIiIiIikim5LwuYlZWFgwcPIjg4GPHx8QAAExMTODs748cff4SampqcIyQiIiIiWSmPU0rkOsIdHR0NGxsb9O/fHzdu3IBIJIJIJMKNGzfg4eGB2rVrIzo6Wp4hEhEREZEMKct4KwvkOsL9cUnAGzduQEdHR6ouPT0dHh4e8PLywsmTJ+UUIRERERHRt5Frwn358mWEhITkSbYBQEdHBzNmzICjo6McIiMiIiKi4sApJSVMT08PsbGxn62PjY2Fnp5eicVDRERERMVLSSCW6VYWyHWEe/DgwfDw8MCUKVPQqlUrGBsbAwASEhJw9uxZzJw5E6NGjZJniERERERE30SuCbe/vz80NTUxf/58jB07FgJB7ncMYrEYJiYmmDhxIiZMmCDPEImIiIhIhvhqdzmYOHEiJk6ciEePHkktC2hpaSnnyIiIiIiIvl2pefGNpaUlnJycIBKJULlyZXmHQ0RERETFQEkg260sKDUJ90ft27fHs2fP5B0GERERERUDJtylgFhcNp42JSIiIiIqjFKXcJdVTRpbY++GcXh4LRAZcTvRqU3DPG2m+HTHw9BAJEdtxtEdk1HdwkSqXl9XExuXeiHh7nq8uL0Oq+YNhaaGsMDjCoWqWDzDE09v/oGkexuxc/UYGBnoSrWpWrkS9m+cgFeRm/D4+mrMnvwzlJU/3fq6tS0QfCwASfc2Yu+GcdDX1ZTUKSsr4fLRWWhYt/rXXJYyjfe0fNq+/ShathwEe/uu6NFjLG7diiqw/fHjl9Cu3S+wt++KTp1G4uLFUKl6sViMpUu3wcXFA3XqdMOAAb8jNva5pD4r6wPGj1+IBg3c0bbtMAQFhUvtv27dfsyYsUZm51fe8H4qHt7Tso8j3KXAmjVrJMsDliWaGkLcjojDmN835Fs/dngnjPBsh9G+6+HaeQrevsvE4W2TIBSqStpsXDYSNjW/ww99ZqPbwPlwcbTGyjlDCjzuvKn90NGtAfoMX4o27v4wNdbHn394S+qVlATYv2kC1NRU0OInPwzxWYW+PZph6tgekjaB84bgYtBdOHXwha62BiaM7CKpGzO0I66ERiH0ZsxXXpmyi/e0/Dl27B8EBKyDl1dvHDiwBNbWlhg0aCpevUrNt/316/cwdux8dO/eBgcPLkWrVt/Dy2sWoqIeS9qsXbsPW7cewbRpI7B79wKoq1fAoEFTkZmZBQDYtesE7t6Nwa5d8+Hu3hZjxy6QfNP35Ek89uw5CW/vfsV+7oqI91Px8J4qBmWBWKZbWVCqEu7MzEx069YNmpqaX25cypy6cBPTF+zGoZOh+dZ7DWqPucsP4MjpMNy5H4fB3oEwNdJH539HTWtZVUbbFvUwYuJaXAuPQdC1SPhM3YwenZ1gaqyfb5862uoY0LMFJs7YiotBd3Hj9iMMHbcGTg1roXF9KwCAm2sd2NT4DgN/XYlbEY9x6sJN+C/cg2EebaCqqgwAsLaqgg07zyH6UTx2/xWEWlZVAAAWZkbo37MF/ObtkvXlKhN4T8ufjRsPwt29Lbp1c4OVlRmmTx+BChWE2LfvdL7tt2w5hKZNG2Dw4K6oXr0qxozpC1vb6ti27QiA3JGzLVsOYfhwd7i5fQ9ra0vMm+eNxMRknDlzBQAQE/MELVs2Ro0a5ujTpyOSk9OQkpIOAJg2bRXGjRsALS2NkrkACob3U/HwnlJZJfeE+/Tp0+jQoQP09fWhoaEBDQ0N6Ovro0OHDjhz5oy8w5MJCzMjmBrp49ylO5Ky9NcZuBYeA0eHGgAAxwY1kZL2BtdvPZS0OXfpNkQiMRrVy/+r//r21aCmpiLVb1TMc8Q9TYJjg4/91sCd+3FIfJkmaXP64k3o6mjAtmZVAMDtiMdo1dQeyspKaOFihzv34wAAy2cPwm+zd+DN2/cyuhKKg/dU8WRlfcDdu9Fwdq4rKVNSUoKzcz3cuBGZ7z7h4ffh5FRPqszFpT7Cw+8DAJ4+TUBSUgqcnT+10dbWRN26NXHjRm4ba2tLhIVF4P37TFy6dB2GhhWhr6+DQ4cuQChURevWTrI90XKC91Px8J4qDiUZb2WBXOPcvHkzOnToAF1dXSxevBhHjhzBkSNHsHjxYujp6aFDhw7YunWrPEOUCRPD3Pm3/02QPn42NtQDABgb6iLpZbpUfU6OCMmpbyRt8us3M/MD0tLf5e3X6N9+jfTyHjcpTXJMABg+cS1+6uCIiH+WICsrG/NX/oXeXV2QkZGJsJsxOLR1Eu78vRh+49yLfO6KivdU8aSkpCMnR4RKlaS/fahUSQ8vX6bku8/Ll6kwMNDLp30qACApKUVS9rk+u3VrDWtrS3ToMAKrVu3GkiUTkJb2BsuWbceUKcOwePFWtG49FIMGTUVCwqtvP9FygvdT8fCeUlkm1xffzJo1C0uWLIGXl1eeugEDBsDFxQX+/v7o1+/zc6MyMzORmZkpVSYW50AgUJZ5vIrqXtRTtHH3l3yuqKeFKT7d0bq7Pxb5D8CVsAfoNXQxLh2ZiWvh0Th25roco6XC4D0tO1RVVeDnN1yqzNd3Cfr164SIiIc4e/YK/vprGdat24eZM9dg+fLJcoqUCoP3U/HwnspeWXnQUZbkOsIdFxcHNze3z9a3atUKT58+LbCPgIAA6OrqSm3Z6RGyDvWbxP87+vj/K00YGegiISkVAJCQlAZDAx2pemVlJVTU05K0ya9foVAVujrSc8eMDHSRkPhvv4mpeY/77yhoQpL0KOlHc6f2w4r1x/EsPhlNnWyx/+gVvMvIxImzN+D6ve0Xz7c84D1VPPr6OlBWVsKrV9IjZa9epcLAIP859wYGn0bKpNvrAQAMDfUlZYXt88qVW3jwIA59+3ZESMhtuLo2hIZGBbRv74KQkDv57kN58X4qHt5TxcFVSkpY7dq1sX79+s/Wb9iwAba2BScDvr6+SEtLk9pUdEpXAhEbl4gXiSlo0cROUqatpY5G9arjatgDAMDV61HQ19VCfftPr7Rv7lwbSkoCXAvPfzWJG7cfIisrW6rfGtVMYfadIa5e/9jvA9hZm8Gw0qfEr1VTe6Slv8O9B3n/mGnepDasrSpj1aZTAABlJSWoquR+W6CqqiK19Fx5xnuqeNTUVFG7thWCg29JykQiEYKDb6J+/Vr57lOvnjWuXLkpVRYUFI569awBAN99ZwxDQ30EB39q8+bNO9y8GYX69a3z9JeZmQV//9Xw9/eCsrIycnJEyM7OBgBkZ+cgJ0f0zedZXvB+Kh7eUyrL5PqbduHChQgMDESdOnXg4+ODuXPnYu7cufDx8UHdunURGBiIxYsXF9iHUCiEjo6O1CaP6SSaGkLUsTVHHVtzAIBFVUPUsTVH1cqVAAAr1x/HxNFd0LG1A2rXqor1i4fjRWIKDp3KXQEjMvo5Tp4Px8o5Q9CwbnU4NayJxTM8sedQMF4k5P41X9lYH+HnFkjWT05/nYFNu85j7pS+cHWyRX17S/yx4BdcCY1CyI1oAMCZv2/h3oOnWL9kBOxtzODmWgd+49yxZsspZGVlS52DUKiKxf6e8Jq0TrLkUXBoJIb1bwN7GzN0ad8YwaH5P5iiiHhPyx9Pzy7YvfskDhw4i5iYJ5g2LRAZGe/RtWvuN3ETJizCwoWbJe09PDrjn3+uY8OGA4iJeYLly3fgzp1o9O37AwBAIBDAw6MzVq3ahbNnryIyMhYTJiyCkVFFuLl9n+f4gYF/olkzB9ja5v730KCBDU6fDsb9+4+wbdsRNGhgUwJXQXHwfioe3lPFUB6XBZTrHO7mzZvjzp07WLVqFa5cuYL4+HgAgImJCdq3b49ffvkFFhYW8gyx0BrUqYZTu6dKPs/z8wAAbN1zEUPHrsbCVYehoS7EioDB0NPRQFBoJDr3m4PMzA+SfTxHr8DiGZ44tvM3iERiHDwegrF+myT1KqrKqGVVBerqapKyCf5bIRKJsXONN4RqKjhz8RZ+/c+60SKRGN0852PprIG4cNAfb99lYvvev+G/cE+ec/htTDecOHcDtyI+rU861m8zNi8fidN7/PDnwcs4cCxEJterLOA9LX86dGiK5OQ0LFu2HUlJKbCxqYZ166ZLvlp+8SIJSv/5/rJBAxssWDAOS5Zsw6JFW2BhURkrV/6GmjXNJW2GDOmGjIz3mDp1BdLT38LBwRbr1k2HUKgmdeyoqMc4fvwSDh5cJilr164JQkJuo0+fSbC0rIKFC8cV8xVQLLyfiof3VDGUlWkgsiQQK+C71NXNess7BCIqQEbcdHmHQERUjtSUdwBS/np8XKb9/WjeXqb9FQe5jnATERERUflSHke45f60VGBgINzc3ODu7o6zZ89K1b18+RLVqlWTU2REREREJGtcpaSELVu2DOPHj4e1tTWEQiE6dOiAgIAASX1OTg4eP35cQA9ERERERKWbXKeUrFmzBmvXrsXPP/8MABg+fDi6dOmCjIwM+Pv7f2FvIiIiIiprlMvIqLQsyTXhfvToEZydnSWfnZ2dce7cObi5ueHDhw8YM2aM/IIjIiIiIpIBuSbcBgYGePLkidTSf3Z2djh37hxatmyJ58+fyy84IiIiIpI5pTKydrYsyXUOt4uLC/bv35+n3NbWFmfPnsXx47JdNoaIiIiI5EtJxltZINcR7kmTJiEsLCzfutq1a+PcuXPYt29fCUdFRERERCQ7ck2469Spgzp16ny23s7ODnZ2diUYEREREREVp7KylJ8slYoX34SEhCA4OFjq1e5OTk5o3LixnCMjIiIiIlniKiUlLDExEV27dkVQUBDMzMxgbGwMAEhISIC3tzeaNGmCffv2wcjISJ5hEhERERF9NbnONR8xYgREIhHu3buH2NhYXL16FVevXkVsbCzu3bsHkUgELy8veYZIRERERDKkJBDLdCsL5DrCffLkSfz999+oVatWnrpatWph2bJlaN68eckHRkRERETFojzO4ZbrCLdQKER6evpn61+/fg2hUFiCERERERERyZZcE+6ePXuif//+OHDggFTinZ6ejgMHDsDT0xO9e/eWY4REREREJEtKAtluZYFcp5QsWrQIIpEIvXr1QnZ2NtTU1AAAWVlZUFFRwaBBg7BgwQJ5hkhERERE9E0EYrFY7rPN09PTERYWJrUsoIODA3R0dL6qP3UzjooTlWYZcdPlHQIRUTlSU94BSLmaeFSm/TkadZRpf8WhVKzDraOjgxYtWsg7DCIiIiIqZoIyMg1EluT+CvqMjAxcunQJEREReerev3+PLVu2yCEqIiIiIiLZkGvCHRUVBRsbG7i6usLe3h7NmjXD8+fPJfVpaWnw9PSUY4REREREJEsCGW9lgVwT7okTJ8LOzg6JiYmIjIyEtrY2XFxcEBcXJ8+wiIiIiKiYCASy3coCuSbcQUFBCAgIgIGBAaysrHD48GG0bdsWTZs2xcOHD+UZGhERERGRTMg14c7IyICKyqfnNgUCAVatWoVOnTqhWbNmiIqKkmN0RERERCRrSjLeygK5rlJibW2N0NBQ2NjYSJWvWLECANC5c2d5hEVEREREJDNy/cPgp59+ws6dO/OtW7FiBXr37o1SsEw4EREREcmIQCCW6VYWlIoX38gaX3xDVLrxxTdERCWpdL34JvzVEZn2V6/SDzLtrziUlakvRERERERlUql40yQRERERlQ9lZSk/WWLCTUREREQlphzm25xSQkRERERUnDjCTUREREQlRqkcDnFzhJuIiIiISoxAxtvXWLlyJSwsLFChQgU4OjoiJCTks23Xrl2Lpk2bQl9fH/r6+nBzcyuwfX6YcBMRERFRubFr1y74+PjAz88P169fR926ddG2bVskJibm2/7ChQvo3bs3zp8/j+DgYFStWhVt2rTBs2fPCn1MrsNNRCWO63ATEZWk0rUOd0SqbNfhttUr2jrcjo6OaNSokeTN5iKRCFWrVsWoUaMwadKkL+6fk5MDfX19rFixAh4eHoU6Jke4iYiIiKhcyMrKQlhYGNzc3CRlSkpKcHNzQ3BwcKH6ePfuHT58+ICKFSsW+rh8aJKIiIiISoysn5nMzMxEZmamVJlQKIRQKMzT9uXLl8jJyYGxsbFUubGxMe7fv1+o402cOBGVK1eWStq/hAk3lQmcgkBERKQYZJ1wBwQEYPp06TzBz88P06ZNk/GRgDlz5uDPP//EhQsXUKFChULvx4SbiIiIiMosX19f+Pj4SJXlN7oNAAYGBlBWVkZCQoJUeUJCAkxMTAo8zoIFCzBnzhycOXMGderUKVKMnMNNRERERCVGSSDbTSgUQkdHR2r7XMKtpqYGBwcHnD17VlImEolw9uxZODk5fTbmefPmYcaMGThx4gQaNmxY5HPmCDcRERERlRh5v/fGx8cH/fv3R8OGDdG4cWMsWbIEb9++haenJwDAw8MDVapUQUBAAABg7ty5mDp1Knbs2AELCwvEx8cDALS0tKClpVWoYzLhJiIiIqJyo2fPnkhKSsLUqVMRHx+PevXq4cSJE5IHKePi4qCk9GkSyKpVq5CVlYXu3btL9VOUeeJch5vKBD40SURE9LVK1zrc0emHZdqflU4nmfZXHDjCTUREREQlRt5TSuSBD00SERERERUjjnATERERUYkRlMMhbo5wExEREREVI45wExEREVGJKY+jvUy4iYiIiKjEcEoJERERERHJFEe4iYiIiKjElMMBbibcRERERFRyOKWkhH348AETJkyAlZUVGjdujA0bNkjVJyQkQFlZWU7RERERERF9O7km3LNmzcKWLVvwyy+/oE2bNvDx8cGwYcOk2ijgm+eJiIiIyi2BjLeyQK5TSrZv345169bhhx9+AAAMGDAA7du3h6enp2S0W1Aev3cgIiIiIoUh1xHuZ8+ewc7OTvLZysoKFy5cQFBQEPr164ecnBw5RkdEREREsqYkkO1WFsg14TYxMUFMTIxUWZUqVXD+/Hlcu3YNAwYMkE9gRERERFQsyuOUErkm3C1btsSOHTvylFeuXBnnzp3Do0eP5BAVEREREZHsyHUO95QpU3D//v1866pUqYKLFy/i9OnTJRwVERERERUXgaD8LYgh14Tb3Nwc5ubmn62vXLky+vfvX4IREREREVFxKivTQGRJ7i++ycrKwsGDBxEcHIz4+HgAuXO7nZ2d8eOPP0JNTU3OERIRERERfT25zuGOjo6GjY0N+vfvjxs3bkAkEkEkEuHGjRvw8PBA7dq1ER0dLc8QiYiIiEiGBALZbmWBXEe4hw8fDnt7e9y4cQM6OjpSdenp6fDw8ICXlxdOnjwppwiJiIiISJbKSI4sU3JNuC9fvoyQkJA8yTYA6OjoYMaMGXB0dJRDZEREREREsiHXKSV6enqIjY39bH1sbCz09PRKLB4iIiIiKl5KMt7KArmOcA8ePBgeHh6YMmUKWrVqBWNjYwBAQkICzp49i5kzZ2LUqFHyDJGIiIiI6JvINeH29/eHpqYm5s+fj7Fjx0Lw78x3sVgMExMTTJw4ERMmTJBniEREREQkQ2XlQUdZEojF4lKx+vijR4+klgW0tLT86r7UzXrLKiwqJTLipss7BCIiojKqprwDkJKceVim/VUUdpJpf8Wh1Ex9sbS0hJOTE0QiESpXrizvcIiIiIiIZKLUJNwftW/fHs+ePZN3GERERERUDAQy/l9ZIPc3Tf6/UjLDhYiIiIiKgUBQ6sZ7i135O+Ni0qSxNfZuGIeH1wKREbcTndo0zNNmik93PAwNRHLUZhzdMRnVLUyk6vV1NbFxqRcS7q7Hi9vrsGreUGhqCAs8rlCoisUzPPH05h9IurcRO1ePgZGBrlSbqpUrYf/GCXgVuQmPr6/G7Mk/Q1n5062vW9sCwccCkHRvI/ZuGAd9XU1JnbKyEi4fnYWGdat/zWUpF7ZvP4qWLQfB3r4revQYi1u3ogpsf/z4JbRr9wvs7buiU6eRuHgxVKpeLBZj6dJtcHHxQJ063TBgwO+IjX0uqc/K+oDx4xeiQQN3tG07DEFB4VL7r1u3HzNmrJHZ+ZU3vJ+KhfdT8fCeUllU6hLuNWvWSJYHLEs0NYS4HRGHMb9vyLd+7PBOGOHZDqN918O18xS8fZeJw9smQShUlbTZuGwkbGp+hx/6zEa3gfPh4miNlXOGFHjceVP7oaNbA/QZvhRt3P1haqyPP//wltQrKQmwf9MEqKmpoMVPfhjiswp9ezTD1LE9JG0C5w3BxaC7cOrgC11tDUwY2UVSN2ZoR1wJjULozZivvDKK7dixfxAQsA5eXr1x4MASWFtbYtCgqXj1KjXf9tev38PYsfPRvXsbHDy4FK1afQ8vr1mIinosabN27T5s3XoE06aNwO7dC6CuXgGDBk1FZmYWAGDXrhO4ezcGu3bNh7t7W4wdu0DyzdCTJ/HYs+ckvL37Ffu5KyLeT8XC+6l4eE8VhUDGW+lXqhLuzMxMdOvWDZqaml9uXMqcunAT0xfsxqGTofnWew1qj7nLD+DI6TDcuR+Hwd6BMDXSR+d/R8JrWVVG2xb1MGLiWlwLj0HQtUj4TN2MHp2dYGqsn2+fOtrqGNCzBSbO2IqLQXdx4/YjDB23Bk4Na6FxfSsAgJtrHdjU+A4Df12JWxGPcerCTfgv3INhHm2gqqoMALC2qoINO88h+lE8dv8VhFpWVQAAFmZG6N+zBfzm7ZL15VIYGzcehLt7W3Tr5gYrKzNMnz4CFSoIsW/f6Xzbb9lyCE2bNsDgwV1RvXpVjBnTF7a21bFt2xEAuSMtW7YcwvDh7nBz+x7W1paYN88biYnJOHPmCgAgJuYJWrZsjBo1zNGnT0ckJ6chJSUdADBt2iqMGzcAWloaJXMBFAzvp2Lh/VQ8vKeKoTzO4ZZ7wn369Gl06NAB+vr60NDQgIaGBvT19dGhQwecOXNG3uHJhIWZEUyN9HHu0h1JWfrrDFwLj4GjQw0AgGODmkhJe4Prtx5K2py7dBsikRiN6uU/naO+fTWoqalI9RsV8xxxT5Pg2OBjvzVw534cEl+mSdqcvngTujoasK1ZFQBwO+IxWjW1h7KyElq42OHO/TgAwPLZg/Db7B148/a9jK6EYsnK+oC7d6Ph7FxXUqakpARn53q4cSMy333Cw+/DyameVJmLS32Eh98HADx9moCkpBQ4O39qo62tibp1a+LGjdw21taWCAuLwPv3mbh06ToMDStCX18Hhw5dgFCoitatnWR7ouUE76di4f1UPLynVJbJ9aHJzZs3Y/DgwejevTsWL14s9abJU6dOoUOHDli/fj369SvbX9WYGObOqf5v0vvxs7GhHgDA2FAXSS/TpepzckRITn0jaZNfv5mZH5CW/i5vv0b/9mukl/e4SWmSYwLA8IlrsXTmQIwZ2hHBoVGYv/Iv9O7qgoyMTITdjMGhrZNQzdwYew4FY/qC3UU+f0WVkpKOnBwRKlWS/gaiUiU9PHz4NN99Xr5MhYGBXp72L1+mAgCSklIkZXnb5NZ169YakZGx6NBhBPT1dbBkyQSkpb3BsmXbsXXrbCxevBXHjv0DMzMTzJ79K4yNK337yZYDvJ+KhfdT8fCeKpKyMSotS3JNuGfNmoUlS5bAy8srT92AAQPg4uICf3//AhPuzMxMZGZmSpWJxTkQCJRlHq+iuhf1FG3c/SWfK+ppYYpPd7Tu7o9F/gNwJewBeg1djEtHZuJaeDSOnbkux2hJVVUFfn7Dpcp8fZegX79OiIh4iLNnr+Cvv5Zh3bp9mDlzDZYvnyynSKkweD8VC++n4uE9JVmQ65SSuLg4uLm5fba+VatWePo0/79aPwoICICurq7Ulp0eIetQv0n8vyPK/796iJGBLhKSUgEACUlpMDTQkapXVlZCRT0tSZv8+hUKVaGrIz13zMhAFwmJ//abmJr3uP+ObCckSY98fzR3aj+sWH8cz+KT0dTJFvuPXsG7jEycOHsDrt/bfvF8ywt9fR0oKyvh1asUqfJXr1JhYJD/vHsDg08jK9Lt9QAAhob6krLC9nnlyi08eBCHvn07IiTkNlxdG0JDowLat3dBSMidfPehvHg/FQvvp+LhPVUcAoGSTLeyQK5R1q5dG+vXr/9s/YYNG2BrW3CC5+vri7S0NKlNRad0JYWxcYl4kZiCFk3sJGXaWupoVK86roY9AABcvR4FfV0t1Lf/9Er75s61oaQkwLXw/FcIuXH7IbKysqX6rVHNFGbfGeLq9Y/9PoCdtRkMK31K5ls1tUda+jvce5D3j5nmTWrD2qoyVm06BQBQVlKCqkrutwWqqipSywmWd2pqqqhd2wrBwbckZSKRCMHBN1G/fq1896lXzxpXrtyUKgsKCke9etYAgO++M4ahoT6Cgz+1efPmHW7ejEL9+tZ5+svMzIK//2r4+3tBWVkZOTkiZGdnAwCys3OQkyP65vMsL3g/FQvvp+LhPVUkXKWkRC1cuBCBgYGoU6cOfHx8MHfuXMydOxc+Pj6oW7cuAgMDsXjx4gL7EAqF0NHRkdrkMZ1EU0OIOrbmqGNrDgCwqGqIOrbmqFo5dy7XyvXHMXF0F3Rs7YDatapi/eLheJGYgkOnclc1iYx+jpPnw7FyzhA0rFsdTg1rYvEMT+w5FIwXCbl/zVc21kf4uQWSNbHTX2dg067zmDulL1ydbFHf3hJ/LPgFV0KjEHIjGgBw5u9buPfgKdYvGQF7GzO4udaB3zh3rNlyCllZ2VLnIBSqYrG/J7wmrZMseRQcGolh/dvA3sYMXdo3RnBo/g+mlFeenl2we/dJHDhwFjExTzBtWiAyMt6ja9fcb24mTFiEhQs3S9p7eHTGP/9cx4YNBxAT8wTLl+/AnTvR6Nv3BwCAQCCAh0dnrFq1C2fPXkVkZCwmTFgEI6OKcHP7Ps/xAwP/RLNmDrC1zf1vokEDG5w+HYz79x9h27YjaNDApgSuguLg/VQsvJ+Kh/eUyiq5zuFu3rw57ty5g1WrVuHKlSuIj48HAJiYmKB9+/b45ZdfYGFhIc8QC61BnWo4tXuq5PM8Pw8AwNY9FzF07GosXHUYGupCrAgYDD0dDQSFRqJzvznIzPwg2cdz9AosnuGJYzt/g0gkxsHjIRjrt0lSr6KqjFpWVaCuriYpm+C/FSKRGDvXeEOopoIzF2/h1/+sBS4SidHNcz6WzhqICwf98fZdJrbv/Rv+C/fkOYffxnTDiXM3cCvi0/qkY/02Y/PykTi9xw9/HryMA8dCZHK9FEWHDk2RnJyGZcu2IykpBTY21bBu3XTJV5EvXiRBSenTX98NGthgwYJxWLJkGxYt2gILi8pYufI31KxpLmkzZEg3ZGS8x9SpK5Ce/hYODrZYt246hEI1qWNHRT3G8eOXcPDgMklZu3ZNEBJyG336TIKlZRUsXDiumK+AYuH9VCy8n4qH91QxlJWl/GRJIFbAd6mrm/WWdwgkYxlx0+UdAhERURlVU94BSHnz4ZxM+9NSbSnT/ooDJ+QSERERERUjuSfcgYGBcHNzg7u7O86ePStV9/LlS1SrVk1OkRERERGR7CnJeCv95BrlsmXLMH78eFhbW0MoFKJDhw4ICAiQ1Ofk5ODx48cF9EBEREREVLrJ9aHJNWvWYO3atfj5558BAMOHD0eXLl2QkZEBf3//L+xNRERERGWNQFD+HpqUa8L96NEjODs7Sz47Ozvj3LlzcHNzw4cPHzBmzBj5BUdERERExYAJd4kyMDDAkydPpJb+s7Ozw7lz59CyZUs8f/6/9u4+KKrrfgP4s4DsgkBCFCFRiE5ABA1QQChiVHzpYqhCag1lTFyQ0qkBm4hpKk0UnbaDSbWlKZboZAo4qWJ9Qahv1BDRKFRZyIICQjFSO6O8mUQDIiB7fn9k3F83rIaXXS+7PB9nZ+Tcc899di+jXy7nnntDunBEREREREYg6RzuuXPn4vDhwwPafX19UVJSghMnTkiQioiIiIhMRWbkP+ZA0ivcGzduRGVlpcFtM2fOxCeffIJDhw495lREREREZDrmsbKIMfHBN2QW+OAbIiKi4RpdD765e/+8Uceztwk36nimIOkV7gcuXryI8vJyvUe7h4WFISQkROJkRERERGRM5jINxJgkLbjb2trwox/9CGVlZfDw8ICrqysAoLW1FevXr0d4eDgOHTqESZMmSRmTiIiIiIxkLC4LKOkkmtdeew1arRb19fVobm7GhQsXcOHCBTQ3N6O+vh5arRbJyclSRiQiIiIiGhFJr3AXFxfj7Nmz8Pb2HrDN29sb77//PhYsWPD4gxERERGRifAK92Mll8tx586dh27/+uuvIZfLH2MiIiIiIiLjkrTgjo2NhUqlQkFBgV7hfefOHRQUFCAhIQFxcVxxhIiIiMhSyGBl1Jc5kHRKyR/+8AdotVr85Cc/wf3792FrawsA6O3thY2NDRITE7F9+3YpIxIRERGRUY29KSWjYh3uO3fuoLKyUm9ZwKCgIDg5OQ1rPK7DbXm4DjcREdFwja51uHv6K4w6ntx6tlHHM4VRsQ63k5MTIiIipI5BRERERCbGZQEl0N3djXPnzqGurm7Atnv37mHPnj0SpCIiIiIi05AZ+TX6SVpwNzY2wsfHB/PmzcPzzz+P+fPn48aNG7rtt2/fRkJCgoQJiYiIiIhGRtKC+1e/+hVmzZqFtrY2NDQ0wNHREXPnzsX169eljEVEREREJjIWVymRNGVZWRkyMjIwceJEeHp64h//+AeUSiVeeOEFfP7551JGIyIiIiIyCkkL7u7ubtjY/P99mzKZDNnZ2Vi2bBnmz5+PxsZGCdMRERERkfGNvTnckq5SMmPGDKjVavj4+Oi1Z2VlAQCWL18uRSwiIiIiMhGZmRTJxiTpFe6XXnoJ+/btM7gtKysLcXFxGAXLhBMRERERDduoePCNsfHBN5aHD74hIiIartH14Jt+UWPU8axlfkYdzxRGxYNviIiIiGisMI+VRYxp7L1jIiIiIqLHiFe4iYiIiOixGYs3TbLgJiIiIqLHaOwV3JxSQkRERERkQiy4iYiIiOixkclkRn0Nx86dOzF16lQoFAqEhobi4sWLj+x/4MABzJgxAwqFAs8//zyOHz8+pOOx4CYiIiKiMWP//v1ITU1Feno6qqqq4O/vD6VSiba2NoP9y8rKEBcXh8TERHz22WeIiYlBTEwMLl++POhjch1uMgtch5uIiGi4Rtc63AINRh1PBu8h9Q8NDcXs2bN1TzbXarVwd3fHunXrsHHjxgH9Y2Nj0dXVhaNHj+ravv/97yMgIAAffPDBoI7JK9xERERE9NjIjPxnKHp7e1FZWYnFixfr2qysrLB48WKUl5cb3Ke8vFyvPwAolcqH9jeEq5QQERERkdnq6elBT0+PXptcLodcLh/Qt6OjA/39/XB1ddVrd3V1xZUrVwyO39LSYrB/S0vLoDNaZMHdfX2f1BFMrqenBxkZGUhLSzP4DUXmh+fUsvB8WhaeT8vDcyol405xycjYgq1b9aeepqenY8uWLUY9zkhwSomZ6unpwdatWwf8REfmi+fUsvB8WhaeT8vDc2o50tLScPv2bb1XWlqawb4TJ06EtbU1Wltb9dpbW1vh5uZmcB83N7ch9TeEBTcRERERmS25XA4nJye918N+a2Fra4ugoCCUlJTo2rRaLUpKShAWFmZwn7CwML3+AHDq1KmH9jfEIqeUEBEREREZkpqaCpVKheDgYISEhCAzMxNdXV1ISEgAAKxevRqTJ09GRkYGAOD111/H/PnzsWPHDkRFRSE/Px9qtRq7d+8e9DFZcBMRERHRmBEbG4v29nZs3rwZLS0tCAgIwMmTJ3U3Rl6/fh1WVv8/CWTOnDnYu3cv3nnnHfz617+Gl5cXjhw5glmzZg36mCy4zZRcLkd6ejpv9LAgPKeWhefTsvB8Wh6e07EtJSUFKSkpBreVlpYOaFu5ciVWrlw57ONZ5INviIiIiIhGC940SURERERkQiy4iYiIiIhMiAU3EREREZEJseA2Uzt37sTUqVOhUCgQGhqKixcvSh2Jhuns2bNYtmwZnnnmGchkMhw5ckTqSDQCGRkZmD17NhwdHTFp0iTExMSgoaFB6lg0TNnZ2fDz89Ot7RsWFoYTJ05IHYuMZNu2bZDJZHjjjTekjkIWjgW3Gdq/fz9SU1ORnp6Oqqoq+Pv7Q6lUoq2tTepoNAxdXV3w9/fHzp07pY5CRnDmzBkkJyfjX//6F06dOoW+vj784Ac/QFdXl9TRaBimTJmCbdu2obKyEmq1GgsXLkR0dDRqa2uljkYjVFFRgV27dsHPz0/qKDQGcJUSMxQaGorZs2cjKysLwDdPSHJ3d8e6deuwceNGidPRSMhkMhQUFCAmJkbqKGQk7e3tmDRpEs6cOYN58+ZJHYeM4KmnnsLvf/97JCYmSh2FhqmzsxOBgYH4y1/+gt/+9rcICAhAZmam1LHIgvEKt5np7e1FZWUlFi9erGuzsrLC4sWLUV5eLmEyIjLk9u3bAL4p0si89ff3Iz8/H11dXUN6pDONPsnJyYiKitL7v5TIlPjgGzPT0dGB/v5+3dOQHnB1dcWVK1ckSkVEhmi1WrzxxhsIDw8f0hPJaHS5dOkSwsLCcO/ePTg4OKCgoAC+vr5Sx6Jhys/PR1VVFSoqKqSOQmMIC24iIhNJTk7G5cuXce7cOamj0Ah4e3tDo9Hg9u3bOHjwIFQqFc6cOcOi2wz997//xeuvv45Tp05BoVBIHYfGEBbcZmbixImwtrZGa2urXntrayvc3NwkSkVE35aSkoKjR4/i7NmzmDJlitRxaARsbW3h6ekJAAgKCkJFRQX+9Kc/YdeuXRIno6GqrKxEW1sbAgMDdW39/f04e/YssrKy0NPTA2trawkTkqXiHG4zY2tri6CgIJSUlOjatFotSkpKOKeQaBQQQiAlJQUFBQX45JNPMG3aNKkjkZFptVr09PRIHYOGYdGiRbh06RI0Go3uFRwcjFWrVkGj0bDYJpPhFW4zlJqaCpVKheDgYISEhCAzMxNdXV1ISEiQOhoNQ2dnJ5qamnRfX7t2DRqNBk899RQ8PDwkTEbDkZycjL1796KwsBCOjo5oaWkBADzxxBOws7OTOB0NVVpaGpYuXQoPDw98/fXX2Lt3L0pLS1FcXCx1NBoGR0fHAfdTjB8/HhMmTOB9FmRSLLjNUGxsLNrb27F582a0tLQgICAAJ0+eHHAjJZkHtVqNiIgI3depqakAAJVKhdzcXIlS0XBlZ2cDABYsWKDXnpOTg/j4+McfiEakra0Nq1evxs2bN/HEE0/Az88PxcXFWLJkidTRiMiMcB1uIiIiIiIT4hxuIiIiIiITYsFNRERERGRCLLiJiIiIiEyIBTcRERERkQmx4CYiIiIiMiEW3EREREREJsSCm4iIiIjIhFhwExERERGZEAtuIrIYMpkMR44csfhjmkp8fDxiYmKkjkFEZHFYcBPRiMTHx0Mmk+HnP//5gG3JycmQyWRGf6T5li1bEBAQYNQxDWlvb8fatWvh4eEBuVwONzc3KJVKnD9/Xtfn5s2bWLp0qcmzfJempiYkJCRgypQpkMvlmDZtGuLi4qBWq6WOBoDFPBGNbSy4iWjE3N3dkZ+fj+7ubl3bvXv3sHfvXnh4eEiYbGRWrFiBzz77DHl5eWhsbERRUREWLFiAW7du6fq4ublBLpdLmBJQq9UICgpCY2Mjdu3ahbq6OhQUFGDGjBnYsGGDpNn6+/uh1WolzUBEJDUW3EQ0YoGBgXB3d8fhw4d1bYcPH4aHhwe+973v6fXt6enBL37xC0yaNAkKhQJz585FRUWFbntpaSlkMhlKSkoQHBwMe3t7zJkzBw0NDQCA3NxcbN26FdXV1ZDJZJDJZMjNzdXt39HRgZdeegn29vbw8vJCUVGRbtuXX36JVatWwcXFBXZ2dvDy8kJOTo7B9/TVV1/h008/xbvvvouIiAg8++yzCAkJQVpaGpYvX67r979TSpqbmyGTyXD48GFERETA3t4e/v7+KC8v1xv7/PnzWLBgAezt7eHs7AylUokvv/wSAKDVapGRkYFp06bBzs4O/v7+OHjw4EM/eyEE4uPj4eXlhU8//RRRUVF47rnnEBAQgPT0dBQWFur6Xrp0CQsXLoSdnR0mTJiAn/3sZ+js7Bww5vbt2/H0009jwoQJSE5ORl9fn95nuHr1ajg7O8Pe3h5Lly7Fv//9b9323NxcPPnkkygqKoKvry/kcjnWrFmDvLw8FBYW6s5ZaWnpQ98TEZGlYcFNREaxZs0aveL1r3/9KxISEgb0e+utt3Do0CHk5eWhqqoKnp6eUCqV+OKLL/T6vf3229ixYwfUajVsbGywZs0aAEBsbCw2bNiAmTNn4ubNm7h58yZiY2N1+23duhUvv/wyampq8OKLL2LVqlW6sTdt2oS6ujqcOHEC9fX1yM7OxsSJEw2+HwcHBzg4OODIkSPo6ekZ0mfx9ttv480334RGo8H06dMRFxeH+/fvAwA0Gg0WLVoEX19flJeX49y5c1i2bBn6+/sBABkZGdizZw8++OAD1NbWYv369XjllVdw5swZg8fSaDSora3Fhg0bYGU18J/0J598EgDQ1dUFpVIJZ2dnVFRU4MCBA/j444+RkpKi1//06dO4evUqTp8+jby8POTm5ur9QBMfHw+1Wo2ioiKUl5dDCIEXX3xRryi/e/cu3n33XXz44Yeora3F+++/j5dffhmRkZG6czZnzpwhfaZERGZNEBGNgEqlEtHR0aKtrU3I5XLR3NwsmpubhUKhEO3t7SI6OlqoVCohhBCdnZ1i3Lhx4m9/+5tu/97eXvHMM8+I9957TwghxOnTpwUA8fHHH+v6HDt2TAAQ3d3dQggh0tPThb+//4AsAMQ777yj+7qzs1MAECdOnBBCCLFs2TKRkJAw6Pd28OBB4ezsLBQKhZgzZ45IS0sT1dXVA45ZUFAghBDi2rVrAoD48MMPddtra2sFAFFfXy+EECIuLk6Eh4cbPN69e/eEvb29KCsr02tPTEwUcXFxBvfZv3+/ACCqqqoe+V52794tnJ2dRWdnp67t2LFjwsrKSrS0tAghvjmXzz77rLh//76uz8qVK0VsbKwQQojGxkYBQJw/f163vaOjQ9jZ2Ym///3vQgghcnJyBACh0Wj0jv/g+4SIaCziFW4iMgoXFxdERUUhNzcXOTk5iIqKGnD1+OrVq+jr60N4eLiubdy4cQgJCUF9fb1eXz8/P93fn376aQBAW1vbd+b43/3Gjx8PJycn3X5r165Ffn4+AgIC8NZbb6GsrOyRY61YsQI3btxAUVERIiMjUVpaisDAQL0rvt+V4dvZH1zhNqSpqQl3797FkiVLdFfYHRwcsGfPHly9etXgPkKIR2Z5oL6+Hv7+/hg/fryuLTw8HFqtVjddBwBmzpwJa2trvfwPstfX18PGxgahoaG67RMmTIC3t7fe+bO1tdX7DIiIxjobqQMQkeVYs2aNborCzp07RzTWuHHjdH+XyWQAMKib7/53vwf7Pthv6dKl+M9//oPjx4/j1KlTWLRoEZKTk7F9+/aHjqdQKLBkyRIsWbIEmzZtwk9/+lOkp6c/cuWVR2W3s7N76H4P5lMfO3YMkydP1tv2sBszp0+fDgC4cuXKgPnyw/Goz2+w7OzsdO+biIg4h5uIjCgyMhK9vb3o6+uDUqkcsP25556Dra2t3rJ6fX19qKiogK+v76CPY2trq5vzPFQuLi5QqVT46KOPkJmZid27dw9pf19fX3R1dQ3r2MA3V79LSkoeOrZcLsf169fh6emp93J3dze4T0BAAHx9fbFjxw6DhfFXX30FAPDx8UF1dbVe9vPnz8PKygre3t6Dyu7j44P79+/jwoULurZbt26hoaHhO8/fSM4ZEZG5Y8FNREZjbW2N+vp61NXV6U1LeGD8+PFYu3YtfvnLX+LkyZOoq6tDUlIS7t69i8TExEEfZ+rUqbh27Ro0Gg06OjoGfVPj5s2bUVhYiKamJtTW1uLo0aPw8fEx2PfWrVtYuHAhPvroI9TU1ODatWs4cOAA3nvvPURHRw8667elpaWhoqICr732GmpqanDlyhVkZ2ejo6MDjo6OePPNN7F+/Xrk5eXh6tWrqKqqwp///Gfk5eUZHE8mkyEnJweNjY144YUXcPz4cXz++eeoqanB7373O13WVatWQaFQQKVS4fLlyzh9+jTWrVuHV199Fa6uroPK7uXlhejoaCQlJeHcuXOorq7GK6+8gsmTJ3/nZzJ16lTU1NSgoaEBHR0dejdZEhFZOhbcRGRUTk5OcHJyeuj2bdu2YcWKFXj11VcRGBiIpqYmFBcXw9nZedDHWLFiBSIjIxEREQEXFxfs27dvUPvZ2toiLS0Nfn5+mDdvHqytrZGfn2+wr4ODA0JDQ/HHP/4R8+bNw6xZs7Bp0yYkJSUhKytr0Fm/bfr06fjnP/+J6upqhISEICwsDIWFhbCx+WaG329+8xts2rQJGRkZ8PHxQWRkJI4dO4Zp06Y9dMyQkBCo1Wp4enoiKSkJPj4+WL58OWpra5GZmQkAsLe3R3FxMb744gvMnj0bP/7xj7Fo0aIhv5ecnBwEBQXhhz/8IcLCwiCEwPHjxwdMRfm2pKQkeHt7Izg4GC4uLnq/5SAisnQyMdg7boiIiIiIaMh4hZuIiIiIyIRYcBMRERERmRALbiIiIiIiE2LBTURERERkQiy4iYiIiIhMiAU3EREREZEJseAmIiIiIjIhFtxERERERCbEgpuIiIiIyIRYcBMRERERmRALbiIiIiIiE2LBTURERERkQv8HVz+LtZo+uoMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Hn3/+qdmzZ2fqn5KSosuXL982BldXV3Xp0kXffvttli8nymqJxOwoWLCgkpKS7tivdOnSqlOnjhYsWGAR2/79+/Xjjz+qffv2d3V+ALgdKtyAAw0aNEhXrlzR448/rurVqystLU1bt27VokWLzK8Rl25Uk99++21FRESYl/krXLiwTpw4oSVLlqhfv34aOnSo3N3dNWrUKA0aNEiPPPKIunbtqpMnT2r+/PmqVKmSVaqeN2U3ppyOWbRoUc2aNUuFCxdWwYIFFRQUpIoVK2bqW6JECXXu3DlT+82K9r/3ZXdZQEl6//33dezYMb388sv67rvv9Oijj8rb21txcXFavHixDh06pKefflqS9Pbbb2vNmjVq0qSJXnrpJeXLl08fffSRUlNTLdYZz66SJUsqIiJCo0ePVtu2bdWpUycdPnxYM2bMUIMGDSwePL2devXqaebMmXr77bdVuXJl+fj4mB9szIlevXpp5MiRmjBhgjp37qznn39eX3/9tV588UVt2LBBjRs3Vnp6ug4dOqSvv/5aq1evNn9CU69ePa1du1aTJk1SmTJlVLFiRQUFBWn8+PHasGGDgoKC1LdvXwUEBOj8+fOKjo7W2rVrdf78+RzHWa9ePS1atEjh4eFq0KCBChUqpI4dO2bZ97333lO7du0UHBysPn36mJcFLFKkiEaNGpXjcwPAHdl5VRQA/7By5Uqjd+/eRvXq1Y1ChQoZ7u7uRuXKlY1BgwYZCQkJmfp/++23RpMmTYyCBQsaBQsWNKpXr24MGDDAOHz4sEW/qVOnGhUqVDA8PDyMhg0bGlu2bDHq1atntG3b1tzn5pJrixcvtjj25vJ1O3futGi/uUTamTNnchzTrZbq69mzZ6al3L7//nsjICDAyJcvX46XCLzdubK7LOBN169fN+bMmWM0bdrUKFKkiOHm5mZUqFDBCAsLy7RkYHR0tBEaGmoUKlTI8PT0NFq2bGls3brVos+t7uvN78O/45o2bZpRvXp1w83NzfD19TX69+9vXLhwIVvXahiGER8fb3To0MEoXLhwpiUhsyLJGDBgQJb7Ro0aZRFjWlqaMWHCBKNGjRqGh4eH4e3tbdSrV88YPXq0kZSUZD7u0KFDRrNmzYwCBQoYkiyWCExISDAGDBhglCtXznBzczNKlSpltGrVyvj4448z3Zt//4xmtdTfpUuXjGeeecYoWrSoIcn8c5VVX8MwjLVr1xqNGzc2ChQoYHh5eRkdO3Y0Dh48aNHnVj/zN7+XJ06cuM0dBYC/mQyDpz6AvC4jI0MlS5bUE088keVUAAAAYDvM4QbymKtXr2aaU71w4UKdP3/+rl7vDQAA7g0VbiCP2bhxowYPHqynnnpKxYsXV3R0tD755BP5+/tr9+7dVluzGQAAZA8PTQJ5jJ+fn8qVK6epU6fq/PnzKlasmHr06KHx48eTbAMA4ABMKQHyGD8/Py1btkzx8fFKS0tTfHy85s6dKx8fH0eHBgCAQ/3000/q2LGjypQpI5PJpKVLl97xmI0bN6pu3bry8PBQ5cqVNX/+/Byfl4QbAAAATuHy5cuqXbu2pk+fnq3+J06cUIcOHdSyZUvFxMTo1Vdf1QsvvKDVq1fn6LzM4QYAAIDTMZlMWrJkSZbvdLhp+PDhWr58ucWLup5++mldvHhRq1atyva5qHADAAAg10pNTVVycrLFlpqaapWxo6KiFBISYtEWGhqqqKioHI2TJx+aLFC+u6NDAAAAuC+kxH3p6BAsWDtPG967mkaPHm3RFhkZaZU3x8bHx8vX19eizdfXV8nJyUpJSVGBAgWyNU6eTLgBAADgHCIiIhQeHm7R5uHh4aBoskbCDQAAALsxmaw7o9nDw8NmCXapUqWUkJBg0ZaQkCAvL69sV7clEm4AAADYkSkXPUIYHBysFStWWLStWbNGwcHBORon91wxAAAAcA8uXbqkmJgYxcTESLqx7F9MTIzi4uIk3Zie0qNHD3P/F198UcePH9ewYcN06NAhzZgxQ19//bUGDx6co/NS4QYAAIDdWHtKSU7s2rVLLVu2NH99c+53z549NX/+fJ0+fdqcfEtSxYoVtXz5cg0ePFgffPCBHnjgAc2ZM0ehoaE5Om+eXIebVUoAAABuuN9WKSnk19Oq4106ucCq49kCFW4AAADYjSMr3I5Cwg0AAAC7MZlMjg7B7pzvTwwAAADAjqhwAwAAwI6cr95Lwg0AAAC7ccY53M53xQAAAIAdUeEGAACA3VDhBgAAAGBVVLgBAABgNyYnrPeScAMAAMBumFICAAAAwKqocAMAAMBunLHCTcINAAAAu3HGhNv5rhgAAACwIyrcAAAAsBuTTI4Owe5IuAEAAGA3TCkBAAAAYFVUuAEAAGA3VLgBAAAAWBUVbgAAANiNM1a4SbgBAABgR86XcDvfFQMAAAB2RIUbAAAAdsOUEgAAAMCGnDHhdr4rBgAAAOyICjcAAADsxuSE9V7nu2IAAADAjqhwAwAAwG6ccQ43CTcAAADsxmQyOToEu3O+PzEAAAAAO6LCDQAAALthSgkAAABgQ6xSAgAAAMCqqHADAADAbphSAgAAANiQMybcznfFAAAAgB1R4QYAAIDd8NAkAAAAAKuiwg0AAAD7ccI53CTcAAAAsBsemgQAAABgVVS4AQAAYDcmk8nRIdgdCTcAAADshlVKAAAAAFgVFW4AAADYjTM+NEnCDQAAAPtxwjnczvcnBgAAAGBHVLgBAABgP05Y7nXCSwYAAADshwo3AAAA7McJ53CTcAMAAMB+nDDhZkoJAAAAYENUuAEAAGA/TljuJeEGAACA3RhMKQEAAABgTVS4AQAAYD/OV+Cmwg0AAADYEhVuAAAA2I+L85W4SbgBAABgPzw0CQAAAMCaqHADAADAfpyvwE3CDQAAADtywjncTCkBAAAAbIgKNwAAAOzHCR+aJOEGAACA/Thfvs2UEgAAAMCWqHADAADAfnhoEgAAAIA1OTzhTklJ0ebNm3Xw4MFM+65evaqFCxc6ICoAAADYhMnKWy7g0IT7yJEj8vf3V7NmzVSzZk01b95cp0+fNu9PSkpSWFiYAyMEAACANRkmk1W33MChCffw4cMVGBioxMREHT58WIULF1bjxo0VFxfnyLAAAACQh02fPl1+fn7Knz+/goKCtGPHjtv2nzJliqpVq6YCBQqoXLlyGjx4sK5evZrt8zk04d66davGjRunEiVKqHLlyvq///s/hYaGqmnTpjp+/LgjQwMAAIAtuJisu+XQokWLFB4ersjISEVHR6t27doKDQ1VYmJilv2/+OILvf7664qMjFRsbKw++eQTLVq0SP/973+zf8k5jtKKUlJSlC/f3wulmEwmzZw5Ux07dlTz5s115MgRB0YHAAAAq3PwHO5Jkyapb9++CgsLU0BAgGbNmiVPT0/NnTs3y/5bt25V48aN9cwzz8jPz09t2rRR9+7d71gV/yeHJtzVq1fXrl27MrVPmzZNjz32mDp16uSAqAAAAJBbpKamKjk52WJLTU3Nsm9aWpp2796tkJAQc5uLi4tCQkIUFRWV5TGNGjXS7t27zQn28ePHtWLFCrVv3z7bMTo04X788cf15ZdfZrlv2rRp6t69uwzDsHNUAAAAsBmTyarbuHHjVKRIEYtt3LhxWZ767NmzSk9Pl6+vr0W7r6+v4uPjszzmmWee0ZgxY9SkSRO5ubmpUqVKatGiRe6ZUhIREaEVK1bccv+MGTOUkZFhx4gAAABgU1aewx0REaGkpCSLLSIiwmrhbty4Ue+8845mzJih6Ohofffdd1q+fLneeuutbI/BmyYBAACQa3l4eMjDwyNbfUuUKCFXV1clJCRYtCckJKhUqVJZHjNixAg9//zzeuGFFyRJNWvW1OXLl9WvXz+98cYbcnG5c/3a4S++uZ1jx47pkUcecXQYAAAAsBYHPjTp7u6uevXqad26dea2jIwMrVu3TsHBwVkec+XKlUxJtaurqyRle+rzfV3hvnTpkjZt2uToMAAAAJBHhIeHq2fPnqpfv74aNmyoKVOm6PLly+aXLfbo0UNly5Y1zwPv2LGjJk2apIceekhBQUE6evSoRowYoY4dO5oT7ztxaMI9derU2+7/888/7RQJAAAA7MLBb4fs1q2bzpw5o5EjRyo+Pl516tTRqlWrzA9SxsXFWVS033zzTZlMJr355pv6888/VbJkSXXs2FFjx47N9jlNhgOXAXFxcVHp0qXl7u6e5f60tDTFx8crPT09R+MWKN/dGuEBAADkeilxWa8I5yiVu3xm1fGOfvucVcezBYdWuCtUqKAJEyaoa9euWe6PiYlRvXr17BwVAAAAYD0OfWiyXr162r179y33m0wm1uEGAADIS1ysvOUCDq1wjxkzRleuXLnl/oCAAJ04ccKOEQEAAMCmHDyH2xEcmnAHBATcdr+bm5sqVKhgp2gAAAAA67vvCvHjx4/XxYsXHR0GAAAAbMGB63A7yn2XcL/zzjs6f/68o8MAAAAArOK+e/END0kCAADkXYZLLilLW9F9V+EG/q1xw+r6Zu5QHd85QylxX6pjm/qZ+owIf1LHd83Q+SMLtPyL/6qSXymL/d5FCmreBwOUcOATnd43RzPf7aeCnh63Pa+Hh5smvxWmP/Z+rDOx8/TlrFflU6KIRZ9yZYrru3nDdO7wfP0WPUvv/PcZubr+/Z9V7Rp+iloxTmdi5+mbuUPlXaSgeZ+rq4u2LB+r+rUr3c1tAWBn/C4CrMRksu6WC9x3CffBgwd5UBIWCnp6aN/BOL365tws9w/p31EvhbXVyxGfqFmnEbp8JVX/99nr8vBwM/eZN3Wg/Ks+oEeffUdder+nJkHVNX1839ue992Rz6tDSF092/8Dtek6RqV9vfXVx4PN+11cTPpu/jC5u+dTy8cj1Td8pp57qrlGDnnK3GfGu321aesBBbePUJHCnho2sLN536v9OmjbriPatffYXd4ZAPbE7yIAd+u+Sbjj4uK0fft2xcfH89AkLPy4ca9GT/xay1bvynL/gD7tNOHDJfphzW7tPxSnFwbPUGkfb3X6X/WpWuUyCm1ZRy8Nn62dMce0dedhhY9coKc6Bau0r3eWY3oVLqBe3Vpq+FufatPWA9qz74T6Df1IwfWrqeFDlSVJIc1qyb/KA+r9ynT9cvA3/bhxr8a8v1j/6dFGbm6ukqTqlctq7pfrdfREvL7+fquqVS4rSfIr76Oe3Voq8t1F1r5dAGyE30WAlfDQpP3NmDFDFSpUUMWKFdWoUSM9/PDD8vHxUZMmTW77UhxAuvGPRWkfb63fvN/clvxXinbGHFNQvSqSpKC6VXUh6ZKifzlu7rN+8z5lZBhqUCfrj1Afqvmg3N3zWYx75Ngpxf1xRkF1b45bRfsPxSnxbJK5z5pNe1XEy1MBVctJkvYd/E2tmtaUq6uLWjYJ1P5DcZKkD9/pozfe+UKXLl+10p0A4Ej8LgJywMVk3S0XcGjCPXHiRI0dO1avvfaaPvroI1WrVk2jRo3S8uXL9eCDD6pZs2batSvrSgIgSaVK3pjH+M9/aG5+7VuyqCTJt2QRnTmbbLE/PT1D5y9eMvfJatzU1GtKSrZ8MVPi2ST5+vxvXJ+imc97Jsl8TknqP3y2Hm8fpIM/T1Fa2nW9N/17dX+iiVJSUrV77zEt+/R17f9psiKHds3xtQO4f/C7CMDtOHSVkunTp2vOnDlq166dJKlZs2Zq1KiR4uPj1bZtW3l7e+u///2vfvzxx1uOkZqaqtTUVIs2w0iXyeRq09iB7Ig98ofadB1j/rpY0UIaEf6kWj85RpPG9NK23b/q6X6TtfmHt7Uz5qhWrI12YLQA8ip+F+G+kksedLQmh1a4ExMT5e/vb/66SpUqSkpK0pkzZyRJvXv3VlRU1G3HGDdunIoUKWKxXU8+aNO4cf+I/18V599P7PuUKKKEMxclSQlnklSyhJfFfldXFxUrWsjcJ6txPTzcVMTLM/O4if8bN/Fi5vP+r5qUcMay2nTThJHPa9onK/Vn/Hk1DQ7Qd8u36UpKqlat26NmD9/+zasA7l/8LgJygDnc9lW1alWtWbPG/PWGDRvk7u6uUqVuLKOUP39+me7wV1BERISSkpIstnxe/LJwFifjEnU68YJaNg40txUuVEAN6lTS9t2/SpK2Rx+Rd5FCeqhmRXOfFo1qyMXFpJ0xWT+Vv2ffcaWlXbcYt8qDpVX+gZLaHn1z3F8VWL28Shb/+x/QVk1rKin5imJ//SPTmC0a11D1ymU0c/6NT2xcXVzklu/GJzFubvkslvACkLvwuwjA7Tj0v6qIiAgNGDBA3bp1U8+ePfXYY4/p5ZdfNifZGzduVGBg4G3H8PDwkJeXl8XGdJK8paCnh2oFVFCtgBvLRfqVK6laARVUrkxxSdL0T1Zq+Mud1aF1PdWoVk6fTO6v04kXtOzHG/P/Dx89pdUbYjR9fF/Vr11JwfWravJbYVq8LEqnEy5Iksr4eitm/UTzOrTJf6Vo/qINmjDiOTULDtBDNSvq44kvatuuI9qx56gkae1Pvyj21z/0yZSXVNO/vEKa1VLk0K76aOGPSku7bnENHh5umjwmTANen2N+uVPUrsP6T882qulfXp3bNVTUrsO2v5kA7hq/iwArccKHJk2Gg1/tuHLlSn322WdKTU1VaGio+vb9ez3Sc+fOSZKKFy+eozELlO9u1RjhWE0f9tePX4/M1P7p4k3qN2SWpBsvm+j9TCsV9fLU1l2H9cobc3X0RLy5r3eRgpr8Vpjah9RVRoahpSt3aEjkfF2+cmP+f/kHSujw1g/VpusY/bwtVtKNf5jGv/mcuj7WSB7u+bR20y965c25Fh/Rli9bQh+M7a1mwQG6fCVVn3/zk94c/6XS0zMsYh0z/Gm55XNVxNjPzW0PVvDVgg8HqsqDZfTV0i0aPGIeb1oF7mP8LkJulRL3paNDsFAp7Gurjnds3v3/sK/DE25bIOEGAAC44b5LuPsstup4xz556s6dHOy+nqh1/fp1xcXFOToMAAAAWIlhsu6WG9zXCfeBAwdUsWLFO3cEAAAA7lMOXYcbAAAATiaXPOhoTQ5NuOvWrXvb/SkpKXaKBAAAAHbhhC++cWjCffDgQT399NO3nDZy+vRpHTlyxM5RAQAAANbj0IQ7MDBQQUFB6t+/f5b7Y2JiNHv2bDtHBQAAAJthSol9NW7cWIcP33qB/cKFC6tZs2Z2jAgAAAA2dV8v2WEbDk24P/jgg9vur1SpkjZs2GCnaAAAAADrY5USAAAA2A8PTTrGjh07FBUVpfj4G6+/LVWqlIKDg9WwYUMHRwYAAADcG4cm3ImJierSpYu2bNmi8uXLy9fXV5KUkJCgwYMHq3Hjxvr222/l4+PjyDABAABgLU740KRDp62/9NJLSk9PV2xsrE6ePKnt27dr+/btOnnypGJjY5WRkaEBAwY4MkQAAABYkWEyWXXLDRxa4V69erV++uknVatWLdO+atWqaerUqWrRooX9AwMAAACsxKEJt4eHh5KTk2+5/6+//pKHh4cdIwIAAIBNOeGygA695G7duqlnz55asmSJReKdnJysJUuWKCwsTN27d3dghAAAALAqF5N1t1zAoRXuSZMmKSMjQ08//bSuX78ud3d3SVJaWpry5cunPn36aOLEiY4MEQAAALgnDp9SMnPmTE2YMEG7d++2WBawXr168vLycmR4AAAAsLZc8qCjNd0X63B7eXmpZcuWjg4DAAAAsDqHT1tPSUnR5s2bdfDgwUz7rl69qoULFzogKgAAANiEE87hdmjCfeTIEfn7+6tZs2aqWbOmmjdvrlOnTpn3JyUlKSwszIERAgAAwKpMVt5yAYcm3MOHD1dgYKASExN1+PBhFS5cWE2aNFFcXJwjwwIAAACsxqFzuLdu3aq1a9eqRIkSKlGihP7v//5PL730kpo2baoNGzaoYMGCjgwPAAAAVmbkkmkg1uTQCndKSory5fs75zeZTJo5c6Y6duyo5s2b68iRIw6MDgAAAFbnhHO4HVrhrl69unbt2iV/f3+L9mnTpkmSOnXq5IiwAAAAAKtxaIX78ccf15dffpnlvmnTpql79+4yDMPOUQEAAMBmTCbrbrmAyciDGW2B8rwOHgAAQJJS4rIubjqK38iVVh3v5Jh2Vh3PFhy+DjcAAACQl90Xb5oEAACAk8gl00CsiQo3AAAAYENUuAEAAGA/uWQpP2si4QYAAID9OGHCzZQSAAAAwIaocAMAAMBuDCd8aJKEGwAAAPbjhPMrnPCSAQAAAPuhwg0AAAD7ccIpJVS4AQAAABuiwg0AAAD7ccJlAUm4AQAAYD9OmHAzpQQAAACwISrcAAAAsB/nK3CTcAMAAMB+DKaUAAAAALAmKtwAAACwHydch5uEGwAAAPbDlBIAAAAA1kSFGwAAAPbjfAVuKtwAAACALVHhBgAAgN24OGG5l4QbAAAAduOEi5QwpQQAAACwpbuqcP/666/asGGDEhMTlZGRYbFv5MiRVgkMAAAAeQ8V7myYPXu2/P39NXLkSH3zzTdasmSJeVu6dKkNQgQAAEBeYTKZrLrdjenTp8vPz0/58+dXUFCQduzYcdv+Fy9e1IABA1S6dGl5eHioatWqWrFiRbbPl+MK99tvv62xY8dq+PDhOT0UAAAAcKhFixYpPDxcs2bNUlBQkKZMmaLQ0FAdPnxYPj4+mfqnpaWpdevW8vHx0TfffKOyZcvqt99+U9GiRbN9zhwn3BcuXNBTTz2V08MAAAAAh08pmTRpkvr27auwsDBJ0qxZs7R8+XLNnTtXr7/+eqb+c+fO1fnz57V161a5ublJkvz8/HJ0zhxPKXnqqaf0448/5vQwAAAAQCaTdbfU1FQlJydbbKmpqVmeOy0tTbt371ZISIi5zcXFRSEhIYqKisrymGXLlik4OFgDBgyQr6+vAgMD9c477yg9PT3b15ytCvfUqVPN/79y5coaMWKEtm3bppo1a5oz/ZtefvnlbJ8cAAAAuBfjxo3T6NGjLdoiIyM1atSoTH3Pnj2r9PR0+fr6WrT7+vrq0KFDWY5//PhxrV+/Xs8++6xWrFiho0eP6qWXXtK1a9cUGRmZrRhNhmEYd+pUsWLF7A1mMun48ePZ6mtLBcp3d3QIAAAA94WUuC8dHYKFqrN/sup4+3oEZapoe3h4yMPDI1PfU6dOqWzZstq6dauCg4PN7cOGDdOmTZu0ffv2zPFWraqrV6/qxIkTcnV1lXRjWsp7772n06dPZyvGbFW4T5w4ka3BAAAAAHu6VXKdlRIlSsjV1VUJCQkW7QkJCSpVqlSWx5QuXVpubm7mZFuS/P39FR8fr7S0NLm7u9/xvDmewz1mzBhduXIlU3tKSorGjBmT0+EAAADgRKw9hzsn3N3dVa9ePa1bt87clpGRoXXr1llUvP+pcePGOnr0qMW7Z44cOaLSpUtnK9mW7iLhHj16tC5dupSp/cqVK5nmzwAAAAD/5GKy7pZT4eHhmj17thYsWKDY2Fj1799fly9fNq9a0qNHD0VERJj79+/fX+fPn9crr7yiI0eOaPny5XrnnXc0YMCAbJ8zx8sCGoaR5SLje/fuVbFixXI6HAAAAGA33bp105kzZzRy5EjFx8erTp06WrVqlflByri4OLm4/F2TLleunFavXq3BgwerVq1aKlu2rF555ZUcvZMmWw9NSpK3t7dMJpOSkpLk5eVlkXSnp6fr0qVLevHFFzV9+vRsn9xWeGgSAADghvvtocmAudZ9aPJg72ZWHc8Wsl3hnjJligzDUO/evTV69GgVKVLEvM/d3V1+fn63nPsCAAAASI5/8Y0jZDvh7tmzp6QbSwQ2atQo0/rbAAAAADLL8Rzu5s2bKyMjQ0eOHFFiYqLFE5uS1KzZ/V/WBwAAgGNk9SxgXpfjhHvbtm165pln9Ntvv+nf079NJlOOXnMJAAAA5HU5TrhffPFF1a9fX8uXL1fp0qWd8q8UAAAA3B1Tjhelzv1ynHD/+uuv+uabb1S5cmVbxAMAAIA8zBlrtTn+GyMoKEhHjx61RSwAAABAnpPjCvegQYM0ZMgQxcfHq2bNmplWK6lVq5bVggMAAEDe4owV7hwn3F26dJEk9e7d29xmMpnMb6DkoUkAAADcCgl3Npw4ccIWcQAAAAB5Uo4T7goVKtgiDgAAADgBFyrc2XPs2DFNmTJFsbGxkqSAgAC98sorqlSpklWDAwAAQN7ijFNKcrxKyerVqxUQEKAdO3aoVq1aqlWrlrZv364aNWpozZo1togRAAAAyLVyXOF+/fXXNXjwYI0fPz5T+/Dhw9W6dWurBQcAAIC8hQp3NsTGxqpPnz6Z2nv37q2DBw9aJSgAAAAgr8hxwl2yZEnFxMRkao+JiZGPj481YgIAAEAeZXIxWXXLDXI8paRv377q16+fjh8/rkaNGkmStmzZogkTJig8PNzqAQIAACDvcMYpJTlOuEeMGKHChQvr/fffV0REhCSpTJkyGjVqlF5++WWrBwgAAADkZibDMIy7Pfivv/6SJBUuXNhqAVlDgfLdHR0CAADAfSEl7ktHh2Ah6JvNVh1v+5NNrDqeLdzVOtw33W+JNgAAAO5vTCm5jUceeSRb/davX3/XwQAAAAB5TbYT7o0bN6pChQrq0KGD3NzcbBkTAAAA8qhcsrCIVWU74Z4wYYLmzZunxYsX69lnn1Xv3r0VGBhoy9gAAACQxzjjlJJsr8P92muv6eDBg1q6dKn++usvNW7cWA0bNtSsWbOUnJxsyxgBAACAXCvHL74JDg7W7Nmzdfr0aQ0YMEBz585VmTJlSLoBAABwRyYX6265wV2HGR0drU2bNik2NlaBgYHM6wYAAACykKOE+9SpU3rnnXdUtWpVPfnkkypWrJi2b9+ubdu2qUCBAraKEQAAAHmEyWTdLTfI9kOT7du314YNG9SmTRu999576tChg/Llu6dlvAEAAOBkTLklS7aibL9p0sXFRaVLl5aPj89tb1R0dLTVgrtbvGkSAADghvvtTZPN/m+LVcf7qWNjq45nC9kuUUdGRtoyDgAAADgBJyxwk3ADAADAfpwx4c4li6kAAAAAuRNPPQIAAMBuqHADAAAAsCoq3ABgIylxox0dAgDcd1yocN/ZwoULlZqamqk9LS1NCxcutEpQAAAAyJtcTNbdcoMcJ9xhYWFKSkrK1P7XX38pLCzMKkEBAAAAeUWOp5QYhpHli2/++OMPFSlSxCpBAQAAIG9yMWXrnYt5SrYT7oceekgmk0kmk0mtWrWyeK17enq6Tpw4obZt29okSAAAAOQNuWUaiDVlO+Hu3LmzJCkmJkahoaEqVKiQeZ+7u7v8/PzUpUsXqwcIAAAA5GY5etNkenq6/Pz81KZNG5UuXdqWcQEAACAPcsY1qXN0za6urvrPf/6jq1ev2ioeAAAA5GEuJsOqW26Q4z8yAgMDdfz4cVvEAgAAAOQ5OU643377bQ0dOlQ//PCDTp8+reTkZIsNAAAAuBVnXIc7x8sCtm/fXpLUqVMni+UBby4XmJ6ebr3oAAAAgFwuxwn3hg0bbBEHAAAAnIAzPjSZ44S7efPmtogDAAAATiC3TAOxphwn3JJ08eJFffLJJ4qNjZUk1ahRQ7179+ZNkwAAAMC/5Liqv2vXLlWqVEmTJ0/W+fPndf78eU2aNEmVKlVSdHS0LWIEAABAHmEyGVbdcoMcV7gHDx6sTp06afbs2ebXu1+/fl0vvPCCXn31Vf30009WDxIAAAB5A1NKsmHXrl0WybYk5cuXT8OGDVP9+vWtGhwAAACQ2+V4SomXl5fi4uIytf/+++8qXLiwVYICAABA3uRi5S03yHGFu1u3burTp48mTpyoRo0aSZK2bNmi1157Td27d7d6gAAAAMg7csvr2K0pxwn3xIkTZTKZ1KNHD12/fl2S5Obmpv79+2v8+PFWDxAAAADIzXKccLu7u+uDDz7QuHHjdOzYMUlSpUqV5OnpafXgAAAAkLfw0GQOeHp6qmbNmtaMBQAAAMhzcpxwX758WePHj9e6deuUmJiojIwMi/3Hjx+3WnAAAADIW3LLg47WlOOE+4UXXtCmTZv0/PPPq3Tp0jKZnPBzAQAAANwVppRkw8qVK7V8+XI1btzYFvEAAAAAeUqOE25vb28VK1bMFrEAAAAgj3PGZQFzPI3mrbfe0siRI3XlyhVbxAMAAIA8zMVk3S03yFaF+6GHHrKYq3306FH5+vrKz89Pbm5uFn2jo6OtGyEAAACQi2Ur4e7cubONwwAAAIAzYJWSW4iMjLR1HAAAAECedNcvvtm9e7diY2MlSTVq1NBDDz1ktaAAAACQNznjQ5M5TrgTExP19NNPa+PGjSpatKgk6eLFi2rZsqW++uorlSxZ0toxAgAAII/ILQ86WlOOp9EMGjRIf/31lw4cOKDz58/r/Pnz2r9/v5KTk/Xyyy/bIkYAAAAg18pxhXvVqlVau3at/P39zW0BAQGaPn262rRpY9XgAAAAkLc4Y4U7xwl3RkZGpqUAJcnNzU0ZGRlWCQoAAAB5kzOuUpLja37kkUf0yiuv6NSpU+a2P//8U4MHD1arVq2sGhwAAABgbdOnT5efn5/y58+voKAg7dixI1vHffXVVzKZTDleMjvHCfe0adOUnJwsPz8/VapUSZUqVVLFihWVnJysDz/8MKfDAQAAwIm4mAyrbjm1aNEihYeHKzIyUtHR0apdu7ZCQ0OVmJh42+NOnjypoUOHqmnTpjk+p8kwjBxHahiG1q5dq0OHDkmS/P39FRISkuOT20qB8t0dHQIAKCVutKNDAABJVR0dgIXw7eutOt6koEdy1D8oKEgNGjTQtGnTJN2YLl2uXDkNGjRIr7/+epbHpKenq1mzZurdu7d+/vlnXbx4UUuXLs32Oe9qHW6TyaTWrVurdevWd3M4AAAAYHdpaWnavXu3IiIizG0uLi4KCQlRVFTULY8bM2aMfHx81KdPH/388885Pm+2p5SsX79eAQEBSk5OzrQvKSlJNWrUuKsAAAAA4DxcrLylpqYqOTnZYktNTc3y3GfPnlV6erp8fX0t2n19fRUfH5/lMZs3b9Ynn3yi2bNn39M1Z8uUKVPUt29feXl5ZdpXpEgR/ec//9GkSZPuOhAAAAAgp8aNG6ciRYpYbOPGjbPK2H/99Zeef/55zZ49WyVKlLjrcbI9pWTv3r2aMGHCLfe3adNGEydOvOtAAAAAkPdZex3uiIgIhYeHW7R5eHhk2bdEiRJydXVVQkKCRXtCQoJKlSqVqf+xY8d08uRJdezY0dx2cxnsfPny6fDhw6pUqdIdY8x2wp2QkJDl+tvmgfLl05kzZ7I7HAAAAJyQ6S5WFrkdDw+PWybY/+bu7q569epp3bp15qX9MjIytG7dOg0cODBT/+rVq2vfvn0WbW+++ab++usvffDBBypXrly2zpvthLts2bLav3+/KleunOX+X375RaVLl87ucAAAAIDdhYeHq2fPnqpfv74aNmyoKVOm6PLlywoLC5Mk9ejRQ2XLltW4ceOUP39+BQYGWhxftGhRScrUfjvZTrjbt2+vESNGqG3btsqfP7/FvpSUFEVGRurRRx/N9okBAADgfBz9avdu3brpzJkzGjlypOLj41WnTh2tWrXK/CBlXFycXFys+z7MbK/DnZCQoLp168rV1VUDBw5UtWrVJEmHDh3S9OnTlZ6erujo6ExPfToC63ADuB+wDjeA+8P9tQ73G7vWWXW8sfXv/zedZ7vC7evrq61bt6p///6KiIjQzTzdZDIpNDRU06dPvy+SbQAAAOB+kqMX31SoUEErVqzQhQsXdPToURmGoSpVqsjb29tW8QEAACAPuZvXsed2d/WmSW9vbzVo0MDasQAAACCPc/Qcbkew7oxwAAAAABbuqsINAAAA3A0q3AAAAACsigo3AAAA7MbV0QE4AAk3AAAA7MYZVylhSgkAAABgQ1S4AQAAYDfO+NAkCTcAAADsxhkTbqaUAAAAADZEhRsAAAB240qFGwAAAIA1UeEGAACA3TjjHG4SbgAAANgN63ADAAAAsCoq3AAAALAbppQAAAAANuTq6AAcgCklAAAAgA1R4QYAAIDdMKUEAAAAsCFWKQEAAABgVVS4AQAAYDe82h0AAACAVVHhBgAAgN3w0CQAAABgQ86YcDOlBAAAALAhKtwAAACwGyrcdnbt2jUNGzZMlStXVsOGDTV37lyL/QkJCXJ1dcYXgAIAAORNribDqltu4NCEe+zYsVq4cKFefPFFtWnTRuHh4frPf/5j0ccwcseNBAAAALLi0Ckln3/+uebMmaNHH31UktSrVy+1a9dOYWFh5mq3yeSEnzsAAADkUc74AKFDr/nPP/9UYGCg+evKlStr48aN2rp1q55//nmlp6c7MDoAAADg3jk04S5VqpSOHTtm0Va2bFlt2LBBO3fuVK9evRwTGAAAAGzCxWTdLTdwaML9yCOP6IsvvsjUXqZMGa1fv14nTpxwQFQAAACwFWdMuB06h3vEiBE6dOhQlvvKli2rTZs2ac2aNXaOCgAAALAehybcFSpUUIUKFW65v0yZMurZs6cdIwIAAIAt5Zal/KzJ4S++SUtL09KlSxUVFaX4+HhJN+Z2N2rUSI899pjc3d0dHCEAAACsJbdMA7Emh87hPnr0qPz9/dWzZ0/t2bNHGRkZysjI0J49e9SjRw/VqFFDR48edWSIAAAAwD1xaIW7f//+qlmzpvbs2SMvLy+LfcnJyerRo4cGDBig1atXOyhCAAAAWJMzVrgdmnBv2bJFO3bsyJRsS5KXl5feeustBQUFOSAyAAAA2IIzJtwOnVJStGhRnTx58pb7T548qaJFi9otHgAAAMDaHFrhfuGFF9SjRw+NGDFCrVq1kq+vryQpISFB69at09tvv61BgwY5MkQAAABYkasTVrgdmnCPGTNGBQsW1HvvvachQ4bIZLrxHTAMQ6VKldLw4cM1bNgwR4YIAAAA3BOTYRj3xWKIJ06csFgWsGLFinc9VoHy3a0VFgDctZS40Y4OAQAkVXV0ABZ+/HOFVcdrU7a9VcezBYfO4f6nihUrKjg4WBkZGSpTpoyjwwEAAIANuFh5yw3uuzjbtWunP//809FhAAAAAFbh8DdN/tt9MsMFAAAANuCMywLedwk3AAAA8i5nXKXkvptS8tFHH5mXBwQkqXHD6vpm7lAd3zlDKXFfqmOb+pn6jAh/Usd3zdD5Iwu0/Iv/qpJfKYv93kUKat4HA5Rw4BOd3jdHM9/tp4KeHrc9r4eHmya/FaY/9n6sM7Hz9OWsV+VToohFn3Jliuu7ecN07vB8/RY9S+/89xm5uv79n1XtGn6KWjFOZ2Ln6Zu5Q+VdpKB5n6uri7YsH6v6tSvdzW0BcJ/6/PPleuSRPqpZ8wk99dQQ/fLLkdv2X7lys9q2fVE1az6hjh0HatOmXRb7DcPQBx98piZNeqhWrS7q1etNnTx5yrw/Le2aXnvtfdWt21Whof/R1q0xFsfPmfOd3nrrI6tdH4Ccu68S7tTUVHXp0kUFCxa8c2c4jYKeHtp3ME6vvjk3y/1D+nfUS2Ft9XLEJ2rWaYQuX0nV/332ujw83Mx95k0dKP+qD+jRZ99Rl97vqUlQdU0f3/e253135PPqEFJXz/b/QG26jlFpX2999fFg834XF5O+mz9M7u751PLxSPUNn6nnnmqukUOeMveZ8W5fbdp6QMHtI1SksKeGDexs3vdqvw7atuuIdu09dpd3BsD9ZsWKnzVu3BwNGNBdS5ZMUfXqFdWnz0idO3cxy/7R0bEaMuQ9PflkGy1d+oFatXpYAwaM1ZEjv5n7zJ79rT799AeNGvWSvv56ogoUyK8+fUYqNTVNkrRo0SodOHBMixa9p65dQzVkyETz9Mzff4/X4sWrNXjw8za/diC7XEyGVbfcwOEJ95o1a9S+fXt5e3vL09NTnp6e8vb2Vvv27bV27VpHh4f7wI8b92r0xK+1bPWuLPcP6NNOEz5coh/W7Nb+Q3F6YfAMlfbxVqf/VcKrVS6j0JZ19NLw2doZc0xbdx5W+MgFeqpTsEr7emc5plfhAurVraWGv/WpNm09oD37Tqjf0I8UXL+aGj5UWZIU0qyW/Ks8oN6vTNcvB3/Tjxv3asz7i/WfHm3k5uYqSapeuazmfrleR0/E6+vvt6pa5bKSJL/yPurZraUi311k7dsFwIHmzVuqrl1D1aVLiCpXLq/Ro19S/vwe+vbbNVn2X7hwmZo2rasXXnhClSqV06uvPqeAgEr67LMfJN2obi9cuEz9+3dVSMjDql69ot59d7ASE89r7dptkqRjx37XI480VJUqFfTssx10/nySLlxIliSNGjVTQ4f2UqFCnva5AUA2uJisu+UGDk24FyxYoPbt26tIkSKaPHmyfvjhB/3www+aPHmyihYtqvbt2+vTTz91ZIi4z/mV91FpH2+t37zf3Jb8V4p2xhxTUL0qkqSgulV1IemSon85bu6zfvM+ZWQYalAn6+kcD9V8UO7u+SzGPXLslOL+OKOgujfHraL9h+KUeDbJ3GfNpr0q4uWpgKrlJEn7Dv6mVk1rytXVRS2bBGr/oThJ0ofv9NEb73yhS5evWulOAHC0tLRrOnDgqBo1qm1uc3FxUaNGdbRnz+Esj4mJOaTg4DoWbU2aPKSYmEOSpD/+SNCZMxfUqNHffQoXLqjatatqz54bfapXr6jduw/q6tVUbd4crZIli8nb20vLlm2Uh4ebWrcOtu6FAsgxhz40OXbsWE2ZMkUDBgzItK9Xr15q0qSJxowZo+ef56MwZK1UyRtzqv+Z9N782rdkUUmSb8kiOnM22WJ/enqGzl+8ZO6T1bipqdeUlHwl87g+/xvXp2jm855JMp9TkvoPn60P3u6tV/t1UNSuI3pv+vfq/kQTpaSkavfeY1r26et6sIKvFi+L0uiJX+f4+gHcPy5cSFZ6eoaKF7f85Kx48aI6fvyPLI85e/aiSpQomqn/2bMXJUlnzlwwt2Xuc2Nfly6tdfjwSbVv/5K8vb00ZcowJSVd0tSpn+vTT9/R5MmfasWKn1W+fCm9884r8vUtfu8XC9yD3FKVtiaHJtxxcXEKCQm55f5WrVppyJAhtx0jNTVVqampFm2GkS6TydUqMQL3IvbIH2rTdYz562JFC2lE+JNq/eQYTRrTS9t2/6qn+03W5h/e1s6Yo1qxNtqB0QLIjdzc8ikysr9FW0TEFD3/fEcdPHhc69Zt0/ffT9WcOd/q7bc/0ocf/tdBkQLOy6FTSmrUqKFPPvnklvvnzp2rgICA244xbtw4FSlSxGK7nnzQ2qHiPhX/v4ryv1cP8SlRRAlnLkqSEs4kqWQJL4v9rq4uKla0kLlPVuN6eLipiJflvEefEkWUkPi/cRMvZj7v/yrbCWcsK983TRj5vKZ9slJ/xp9X0+AAfbd8m66kpGrVuj1q9vDtf9YB3N+8vb3k6uqic+cuWLSfO3dRJUpk/bxIiRJ/V7Mt+xeVJJUs6W1uy+6Y27b9ol9/jdNzz3XQjh371KxZfXl65le7dk20Y8f+LI8B7Ik3TdrZ+++/rxkzZqhWrVoKDw/XhAkTNGHCBIWHh6t27dqaMWOGJk+efNsxIiIilJSUZLHl8yJxcRYn4xJ1OvGCWjYONLcVLlRADepU0vbdv0qStkcfkXeRQnqoZkVznxaNasjFxaSdMVmvELJn33GlpV23GLfKg6VV/oGS2h59c9xfFVi9vEoW/zuZb9W0ppKSryj218wfH7doXEPVK5fRzPk/SpJcXVzklu/GJzFubvkslhMEkPu4u7upRo3Kior6xdyWkZGhqKi9euihalkeU6dOdW3btteibevWGNWpU12S9MADvipZ0ltRUX/3uXTpivbuPaKHHqqeabzU1DSNGTNLY8YMkKurq9LTM3T9+nVJ0vXr6UpPz7jn6wTulclk3S03cOi/8C1atND+/fvVrl077d69W3PnztXcuXO1e/dutWvXTvv27VOzZs1uO4aHh4e8vLwsNqaT5C0FPT1UK6CCagVUkCT5lSupWgEVVK7MjXmI0z9ZqeEvd1aH1vVUo1o5fTK5v04nXtCyH2+sanL46Cmt3hCj6eP7qn7tSgquX1WT3wrT4mVROp1woxJVxtdbMesnmtfETv4rRfMXbdCEEc+pWXCAHqpZUR9PfFHbdh3Rjj1HJUlrf/pFsb/+oU+mvKSa/uUV0qyWIod21UcLf1Ra2nWLa/DwcNPkMWEa8Poc83JdUbsO6z8926imf3l1btdQUbuyfqgKQO4RFtZZX3+9WkuWrNOxY79r1KgZSkm5qieeuDF9ctiwSXr//QXm/j16dNLPP0dr7twlOnbsd3344Rfav/+onnvuUUmSyWRSjx6dNHPmIq1bt12HD5/UsGGT5ONTTCEhD2c6/4wZX6l583oKCLjxu6xuXX+tWROlQ4dO6LPPflDduv52uAsA/s3hb5r08/PThAkTHB0G7mN1az2oH78eaf763cgekqRPF29SvyGz9P7M/5NnAQ9NG/eCinp5auuuw+r0/Hilpl4zHxP28jRNfitMK758QxkZhpau3KEhkfPN+/O5uapa5bIqUMDd3DZszKfKyDD05UeD5eGeT2s3/aJX/rEWeEaGoS5h7+mDsb21cekYXb6Sqs+/+Ulj3l+c6RreeLWLVq3fo18O/r227pDIBVrw4UCtWRypr5Zu0ZIVO6xyvwA4Tvv2TXX+fJKmTv1cZ85ckL//g5ozZ7R5+sfp02fk8o8nxurW9dfEiUM1ZcpnmjRpofz8ymj69DdUtWoFc5++fbsoJeWqRo6cpuTky6pXL0Bz5oyWh4e7xbmPHPlNK1du1tKlU81tbds21o4d+/Tss6+rYsWyev/9oTa+A8Cd5ZKitFWZjJvltjykQPnujg4BAJQSN9rRIQCApKqODsDCrrPLrTpe/RIdrDqeLTh80uiMGTMUEhKirl27at26dRb7zp49qwcffNBBkQEAAAD3zqEJ99SpU/Xaa6+pevXq8vDwUPv27TVu3Djz/vT0dP3222+3GQEAAAC5iTOuUuLQOdwfffSRZs+erWeeeUaS1L9/f3Xu3FkpKSkaM2bMHY4GAAAA7n8OTbhPnDihRo0amb9u1KiR1q9fr5CQEF27dk2vvvqq44IDAACA1ZlMee7xwTtyaMJdokQJ/f777/Lz8zO3BQYGav369XrkkUd06tQpxwUHAAAAq3PGVUocOvWlSZMm+u677zK1BwQEaN26dVq5cqUDogIAAACsx6EV7tdff127d+/Ocl+NGjW0fv16ffvtt3aOCgAAALaSW94OaU0OTbhr1aqlWrVq3XJ/YGCgAgMDb7kfAAAAuYsT5tuOf9OkJO3YsUNRUVGKj4+XJJUqVUrBwcFq2LChgyMDAAAA7o1DE+7ExEQ98cQT2rp1q8qXLy9fX19JUkJCggYPHqzGjRvr22+/lY+PjyPDBAAAgJW4OGGJ26EPTb700kvKyMhQbGysTp48qe3bt2v79u06efKkYmNjlZGRoQEDBjgyRAAAAFiRycrb3Zg+fbr8/PyUP39+BQUFaceOHbfsO3v2bDVt2lTe3t7y9vZWSEjIbftnxaEJ9+rVqzV9+nRVq1Yt075q1app6tSpWrVqlQMiAwAAQF60aNEihYeHKzIyUtHR0apdu7ZCQ0OVmJiYZf+NGzeqe/fu2rBhg6KiolSuXDm1adNGf/75Z7bP6dCE28PDQ8nJybfc/9dff8nDw8OOEQEAAMCWTCbrbjk1adIk9e3bV2FhYQoICNCsWbPk6empuXPnZtn/888/10svvaQ6deqoevXqmjNnjjIyMrRu3bpsn9OhCXe3bt3Us2dPLVmyxCLxTk5O1pIlSxQWFqbu3bs7MEIAAADkFWlpadq9e7dCQkLMbS4uLgoJCVFUVFS2xrhy5YquXbumYsWKZfu8Dn1octKkScrIyNDTTz+t69evy93dXdKNm5EvXz716dNHEydOdGSIAAAAsCJrPzOZmpqq1NRUizYPD48sZ0mcPXtW6enp5oU6bvL19dWhQ4eydb7hw4erTJkyFkn7nTg04fbw8NDMmTM1YcIE7d6922JZwHr16snLy8uR4QEAAMDKrJ1wjxs3TqNHj7Zoi4yM1KhRo6x8Jmn8+PH66quvtHHjRuXPnz/bx90X63B7eXmpZcuWjg4DAAAAuUxERITCw8Mt2m71DGCJEiXk6uqqhIQEi/aEhASVKlXqtueZOHGixo8fr7Vr1972xY1ZcegcbklKSUnR5s2bdfDgwUz7rl69qoULFzogKgAAANiCi8m6m4eHh7y8vCy2WyXc7u7uqlevnsUDjzcfgAwODr5lzO+++67eeustrVq1SvXr18/5Nef4CCs6cuSI/P391axZM9WsWVPNmzfXqVOnzPuTkpIUFhbmwAgBAABgTY5ehzs8PFyzZ8/WggULFBsbq/79++vy5cvmnLNHjx6KiIgw958wYYJGjBihuXPnys/PT/Hx8YqPj9elS5eyfU6HJtzDhw9XYGCgEhMTdfjwYRUuXFhNmjRRXFycI8MCAABAHtWtWzdNnDhRI0eOVJ06dRQTE6NVq1aZH6SMi4vT6dOnzf1nzpyptLQ0PfnkkypdurR5y8nCHibDMAyrX0k2+fr6au3atapZs6YkyTAMvfTSS1qxYoU2bNigggULqkyZMkpPT8/RuAXKs5QgAMdLiRt9504AYHNVHR2AhaPJ/2fV8Sp7dbTqeLbg0Ap3SkqK8uX7+7lNk8mkmTNnqmPHjmrevLmOHDniwOgAAABgbY6eUuIIDl2lpHr16tq1a5f8/f0t2qdNmyZJ6tSpkyPCAgAAAKzGoRXuxx9/XF9++WWW+6ZNm6bu3bvLgTNeAAAAYGWOfrW7Izh0DretMIcbwP2AOdwA7g/31xzu439Zdw73g4Xv/znc98WLbwAAAOAcHP4SGAcg4QYAAIDd5JZpINbkjH9kAAAAAHZDhRsAAAB244QFbhJuAAAA2A9TSgAAAABYFRVuAAAA2I0TFripcAMAAAC2RIUbAAAAduPihCVuEm4AAADYjRPm20wpAQAAAGyJCjcAAADsxmQyHB2C3ZFwAwAAwG6YUgIAAADAqqhwAwAAwG6c8U2TJNwAAACwGyfMt5lSAgAAANgSFW4AAADYjTNWe53xmgEAAAC7ocINAAAAu+GhSQAAAMCmnC/jZkoJAAAAYENUuAEAAGA3JiescJNwAwAAwG5MJuebYOF8VwwAAADYERVuAAAA2BFTSgAAAACbccY53EwpAQAAAGyICjcAAADsiAo3AAAAACuiwg0AAAC7ccZlAUm4AQAAYEdMKQEAAABgRVS4AQAAYDfOuCwgCTcAAADsxhkTbqaUAAAAADZEhRsAAAB25Hz1Xue7YgAAAMCOqHADAADAbkwm55vDTcINAAAAO3K+hJspJQAAAIANUeEGAACA3TjjsoAk3AAAALAj55tg4XxXDAAAANgRFW4AAADYDVNKAAAAABtyxmUBmVICAAAA2BAVbgAAANgRFW4AAAAAVkSFGwAAAHZjcsJ6Lwk3AAAA7IgpJQAAAACsiAo3AAAA7MYZlwUk4QYAAIAdOV/CzZQSAAAAwIaocAMAAMBunHGVEue7YgAAAMCOqHADAADAjpxvDjcJNwAAAOzG5IQJN1NKAAAAABuiwg0AAAC7YR1uAAAAwKacb4KF810xAAAAYEdUuAEAAGA3zvjQJAk3AAAA7Mj5Em6mlAAAAAA2RMINAAAAuzGZTFbd7sb06dPl5+en/PnzKygoSDt27Lht/8WLF6t69erKnz+/atasqRUrVuTofCTcAAAAcBqLFi1SeHi4IiMjFR0drdq1ays0NFSJiYlZ9t+6dau6d++uPn36aM+ePercubM6d+6s/fv3Z/ucJsMwDGtdwP2iQPnujg4BAJQSN9rRIQCApKqODsCCocNWHc+kajnqHxQUpAYNGmjatGmSpIyMDJUrV06DBg3S66+/nql/t27ddPnyZf3www/mtocfflh16tTRrFmzsnVOKtwAAACwG5OV/5cTaWlp2r17t0JCQsxtLi4uCgkJUVRUVJbHREVFWfSXpNDQ0Fv2zwqrlAAAACDXSk1NVWpqqkWbh4eHPDw8MvU9e/as0tPT5evra9Hu6+urQ4cOZTl+fHx8lv3j4+OzHWOeTLhT4r50dAjI5VJTUzVu3DhFRERk+R8sANgav4eQd1l3isu4caM0erTlFL7IyEiNGjXKque5F0wpAbKQmpqq0aNHZ/qLGQDshd9DQPZEREQoKSnJYouIiMiyb4kSJeTq6qqEhASL9oSEBJUqVSrLY0qVKpWj/lkh4QYAAECu5eHhIS8vL4vtVp8Kubu7q169elq3bp25LSMjQ+vWrVNwcHCWxwQHB1v0l6Q1a9bcsn9W8uSUEgAAACAr4eHh6tmzp+rXr6+GDRtqypQpunz5ssLCwiRJPXr0UNmyZTVu3DhJ0iuvvKLmzZvr/fffV4cOHfTVV19p165d+vjjj7N9ThJuAAAAOI1u3brpzJkzGjlypOLj41WnTh2tWrXK/GBkXFycXFz+ngTSqFEjffHFF3rzzTf13//+V1WqVNHSpUsVGBiY7XPmyXW4gXvFw0oAHI3fQ0DeQcINAAAA2BAPTQIAAAA2RMINAAAA2BAJNwAAAGBDJNzAv0yfPl1+fn7Knz+/goKCtGPHDkeHBMCJjBs3Tg0aNFDhwoXl4+Ojzp076/Dhw44OC8A9IOEG/mHRokUKDw9XZGSkoqOjVbt2bYWGhioxMdHRoQFwEps2bdKAAQO0bds2rVmzRteuXVObNm10+fJlR4cG4C6xSgnwD0FBQWrQoIGmTZsm6cbbp8qVK6dBgwbp9ddfd3B0AJzRmTNn5OPjo02bNqlZs2aODgfAXaDCDfxPWlqadu/erZCQEHObi4uLQkJCFBUV5cDIADizpKQkSVKxYsUcHAmAu0XCDfzP2bNnlZ6ebn7T1E2+vr6Kj493UFQAnFlGRoZeffVVNW7cOEdvtQNwf+HV7gAA3KcGDBig/fv3a/PmzY4OBcA9IOEG/qdEiRJydXVVQkKCRXtCQoJKlSrloKgAOKuBAwfqhx9+0E8//aQHHnjA0eEAuAdMKQH+x93dXfXq1dO6devMbRkZGVq3bp2Cg4MdGBkAZ2IYhgYOHKglS5Zo/fr1qlixoqNDAnCPqHAD/xAeHq6ePXuqfv36atiwoaZMmaLLly8rLCzM0aEBcBIDBgzQF198oe+//16FCxc2P0NSpEgRFShQwMHRAbgbLAsI/Mu0adP03nvvKT4+XnXq1NHUqVMVFBTk6LAAOAmTyZRl+7x589SrVy/7BgPAKki4AQAAABtiDjcAAABgQyTcAAAAgA2RcAMAAAA2RMINAAAA2BAJNwAAAGBDJNwAAACADZFwAwAAADZEwg0AAADYEAk3gDzDZDJp6dKlef6cttKrVy917tzZ0WEAQJ5Dwg3gnvTq1Usmk0kvvvhipn0DBgyQyWSy+uuoR40apTp16lh1zKycOXNG/fv3V/ny5eXh4aFSpUopNDRUW7ZsMfc5ffq02rVrZ/NY7uTo0aMKCwvTAw88IA8PD1WsWFHdu3fXrl27HB2aJJJ5AM6NhBvAPStXrpy++uorpaSkmNuuXr2qL774QuXLl3dgZPemS5cu2rNnjxYsWKAjR45o2bJlatGihc6dO2fuU6pUKXl4eDgwSmnXrl2qV6+ejhw5oo8++kgHDx7UkiVLVL16dQ0ZMsShsaWnpysjI8OhMQCAo5FwA7hndevWVbly5fTdd9+Z27777juVL19eDz30kEXf1NRUvfzyy/Lx8VH+/PnVpEkT7dy507x/48aNMplMWrdunerXry9PT081atRIhw8fliTNnz9fo0eP1t69e2UymWQymTR//nzz8WfPntXjjz8uT09PValSRcuWLTPvu3Dhgp599lmVLFlSBQoUUJUqVTRv3rwsr+nixYv6+eefNWHCBLVs2VIVKlRQw4YNFRERoU6dOpn7/XNKycmTJ2UymfTdd9+pZcuW8vT0VO3atRUVFWUx9pYtW9SiRQt5enrK29tboaGhunDhgiQpIyND48aNU8WKFVWgQAHVrl1b33zzzS3vvWEY6tWrl6pUqaKff/5ZHTp0UKVKlVSnTh1FRkbq+++/N/fdt2+fHnnkERUoUEDFixdXv379dOnSpUxjTpw4UaVLl1bx4sU1YMAAXbt2zeIe9ujRQ97e3vL09FS7du3066+/mvfPnz9fRYsW1bJlyxQQECAPDw/17t1bCxYs0Pfff2/+nm3cuPGW1wQAeQ0JNwCr6N27t0XyOnfuXIWFhWXqN2zYMH377bdasGCBoqOjVblyZYWGhur8+fMW/d544w29//772rVrl/Lly6fevXtLkrp166YhQ4aoRo0aOn36tE6fPq1u3bqZjxs9erS6du2qX375Re3bt9ezzz5rHnvEiBE6ePCgVq5cqdjYWM2cOVMlSpTI8noKFSqkQoUKaenSpUpNTc3RvXjjjTc0dOhQxcTEqGrVqurevbuuX78uSYqJiVGrVq0UEBCgqKgobd68WR07dlR6erokady4cVq4cKFmzZqlAwcOaPDgwXruuee0adOmLM8VExOjAwcOaMiQIXJxyfwrvWjRopKky5cvKzQ0VN7e3tq5c6cWL16stWvXauDAgRb9N2zYoGPHjmnDhg1asGCB5s+fb/EHTa9evbRr1y4tW7ZMUVFRMgxD7du3t0jKr1y5ogkTJmjOnDk6cOCApk6dqq5du6pt27bm71mjRo1ydE8BIFczAOAe9OzZ03jssceMxMREw8PDwzh58qRx8uRJI3/+/MaZM2eMxx57zOjZs6dhGIZx6dIlw83Nzfj888/Nx6elpRllypQx3n33XcMwDGPDhg2GJGPt2rXmPsuXLzckGSkpKYZhGEZkZKRRu3btTLFIMt58803z15cuXTIkGStXrjQMwzA6duxohIWFZfvavvnmG8Pb29vInz+/0ahRIyMiIsLYu3dvpnMuWbLEMAzDOHHihCHJmDNnjnn/gQMHDElGbGysYRiG0b17d6Nx48ZZnu/q1auGp6ensXXrVov2Pn36GN27d8/ymEWLFhmSjOjo6Ntey8cff2x4e3sbly5dMrctX77ccHFxMeLj4w3DuPG9rFChgnH9+nVzn6eeesro1q2bYRiGceTIEUOSsWXLFvP+s2fPGgUKFDC+/vprwzAMY968eYYkIyYmxuL8N39OAMAZUeEGYBUlS5ZUhw4dNH/+fM2bN08dOnTIVD0+duyYrl27psaNG5vb3Nzc1LBhQ8XGxlr0rVWrlvn/ly5dWpKUmJh4xzj+eVzBggXl5eVlPq5///766quvVKdOHQ0bNkxbt2697VhdunTRqVOntGzZMrVt21YbN25U3bp1LSq+d4rh37HfrHBn5ejRo7py5Ypat25trrAXKlRICxcu1LFjx7I8xjCM28ZyU2xsrGrXrq2CBQua2xo3bqyMjAzzdB1JqlGjhlxdXS3ivxl7bGys8uXLp6CgIPP+4sWLq1q1ahbfP3d3d4t7AADOLp+jAwCQd/Tu3ds8RWH69On3NJabm5v5/5tMJknK1sN3/zzu5rE3j2vXrp1+++03rVixQmvWrFGrVq00YMAATZw48Zbj5c+fX61bt1br1q01YsQIvfDCC4qMjLztyiu3i71AgQK3PO7mfOrly5erbNmyFvtu9WBm1apVJUmHDh3KNF/+btzu/mVXgQIFzNcNAGAONwAratu2rdLS0nTt2jWFhoZm2l+pUiW5u7tbLKt37do17dy5UwEBAdk+j7u7u3nOc06VLFlSPXv21GeffaYpU6bo448/ztHxAQEBunz58l2dW7pR/V63bt0tx/bw8FBcXJwqV65ssZUrVy7LY+rUqaOAgAC9//77WSbGFy9elCT5+/tr7969FrFv2bJFLi4uqlatWrZi9/f31/Xr17V9+3Zz27lz53T48OE7fv/u5XsGALkdCTcAq3F1dVVsbKwOHjxoMS3hpoIFC6p///567bXXtGrVKh08eFB9+/bVlStX1KdPn2yfx8/PTydOnFBMTIzOnj2b7YcaR44cqe+//15Hjx7VgQMH9MMPP8jf3z/LvufOndMjjzyizz77TL/88otOnDihxYsX691339Vjjz2W7Vj/LSIiQjt37tRLL72kX375RYcOHdLMmTN19uxZFS5cWEOHDtXgwYO1YMECHTt2TNHR0frwww+1YMGCLMczmUyaN2+ejhw5oqZNm2rFihU6fvy4fvnlF40dO9Yc67PPPqv8+fOrZ8+e2r9/vzZs2KBBgwbp+eefl6+vb7Zir1Klih577DH17dtXmzdv1t69e/Xcc8+pbNmyd7wnfn5++uWXX3T48GGdPXvW4iFLAMjrSLgBWJWXl5e8vLxuuX/8+PHq0qWLnn/+edWtW1dHjx7V6tWr5e3tne1zdOnSRW3btlXLli1VsmRJffnll9k6zt3dXREREapVq5aaNWsmV1dXffXVV1n2LVSokIKCgjR58mQ1a9ZMgYGBGjFihPr27atp06ZlO9Z/q1q1qn788Uft3btXDRs2VHBwsL7//nvly3djht9bb72lESNGaNy4cfL391fbtm21fPlyVaxY8ZZjNmzYULt27VLlypXVt29f+fv7q1OnTjpw4ICmTJkiSfL09NTq1at1/vx5NWjQQE8++aRatWqV42uZN2+e6tWrp0cffVTBwcEyDEMrVqzINBXl3/r27atq1aqpfv36KlmypMWnHACQ15mM7D5xAwAAACDHqHADAAAANkTCDQAAANgQCTcAAABgQyTcAAAAgA2RcAMAAAA2RMINAAAA2BAJNwAAAGBDJNwAAACADZFwAwAAADZEwg0AAADYEAk3AAAAYEMk3AAAAIAN/T+LqVjUVbODIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "segments = sorted(purchase['cluster'].unique())\n",
    "\n",
    "for seg in segments:\n",
    "    seg_df = purchase[purchase['cluster'] == seg]\n",
    "    \n",
    "    # cohort matrix\n",
    "    cohort_matrix = (\n",
    "        seg_df.groupby(['cohort_month', 'period_index'])['user_id']\n",
    "              .nunique()\n",
    "              .unstack(fill_value=0)\n",
    "    )\n",
    "    cohort_sizes  = cohort_matrix.iloc[:, 0]\n",
    "    retention     = cohort_matrix.divide(cohort_sizes, axis=0)\n",
    "    \n",
    "    # 绘图 / Plot\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    sns.heatmap(\n",
    "        retention,\n",
    "        annot=True,\n",
    "        fmt=\".2%\",\n",
    "        cmap=\"YlGnBu\",\n",
    "        cbar=True\n",
    "    )\n",
    "    plt.title(f'Segment {seg}: Cohort Retention')\n",
    "    plt.xlabel('Months Since Cohort')\n",
    "    plt.ylabel('Cohort Month')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25663de7",
   "metadata": {
    "papermill": {
     "duration": 0.015258,
     "end_time": "2025-09-14T10:27:06.459057",
     "exception": false,
     "start_time": "2025-09-14T10:27:06.443799",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3.3 转化率漏斗 / Conversion Funnel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c5c7d67d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:27:06.491082Z",
     "iopub.status.busy": "2025-09-14T10:27:06.490731Z",
     "iopub.status.idle": "2025-09-14T10:27:09.002355Z",
     "shell.execute_reply": "2025-09-14T10:27:09.001414Z"
    },
    "papermill": {
     "duration": 2.529732,
     "end_time": "2025-09-14T10:27:09.003868",
     "exception": false,
     "start_time": "2025-09-14T10:27:06.474136",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 统计月度各阶段数量 / Monthly stage counts per cluster\n",
    "stage_counts = (\n",
    "    df_raw\n",
    "    .groupby(['event_month', 'cluster'])\n",
    "    .agg(\n",
    "        view_cnt    = ('is_view',     'sum'),\n",
    "        cart_cnt    = ('is_cart',     'sum'),\n",
    "        purchase_cnt= ('is_purchase', 'sum')\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# 计算三种转化率，分母为 0 时结果设为 0\n",
    "# Compute three types of conversion rates; set result to 0 if the denominator is 0\n",
    "stage_counts['view_to_cart']     = np.where(\n",
    "    stage_counts['view_cnt'] > 0,\n",
    "    stage_counts['cart_cnt'] / stage_counts['view_cnt'],\n",
    "    0.0\n",
    ")\n",
    "\n",
    "stage_counts['cart_to_purchase'] = np.where(\n",
    "    stage_counts['cart_cnt'] > 0,\n",
    "    stage_counts['purchase_cnt'] / stage_counts['cart_cnt'],\n",
    "    0.0\n",
    ")\n",
    "\n",
    "stage_counts['view_to_purchase'] = np.where(\n",
    "    stage_counts['view_cnt'] > 0,\n",
    "    stage_counts['purchase_cnt'] / stage_counts['view_cnt'],\n",
    "    0.0\n",
    ")\n",
    "\n",
    "# 每种转化率单独作图：多簇折线对比\n",
    "# Plot each conversion rate separately: multi-cluster line comparison\n",
    "rate_cols = {\n",
    "    'view_to_cart'     : 'View-to-Cart Rate',\n",
    "    'cart_to_purchase' : 'Cart-to-Purchase Rate',\n",
    "    'view_to_purchase' : 'View-to-Purchase Rate'\n",
    "}\n",
    "\n",
    "for col, title in rate_cols.items():\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    \n",
    "    for seg in sorted(stage_counts['cluster'].unique()):\n",
    "        seg_df = stage_counts[stage_counts['cluster'] == seg].copy()\n",
    "        seg_df.sort_values('event_month', inplace=True)\n",
    "        \n",
    "        plt.plot(\n",
    "            seg_df['event_month'].astype(str),\n",
    "            seg_df[col],\n",
    "            marker='o',\n",
    "            label=f'Segment {seg}'\n",
    "        )\n",
    "    \n",
    "    plt.title(f'Monthly {title}')\n",
    "    plt.xlabel('Month')\n",
    "    plt.ylabel('Conversion Rate')\n",
    "    plt.ylim(0, 1)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    # plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "21b38c79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-14T10:27:09.042743Z",
     "iopub.status.busy": "2025-09-14T10:27:09.041913Z",
     "iopub.status.idle": "2025-09-14T10:27:09.067218Z",
     "shell.execute_reply": "2025-09-14T10:27:09.066455Z"
    },
    "papermill": {
     "duration": 0.046264,
     "end_time": "2025-09-14T10:27:09.068688",
     "exception": false,
     "start_time": "2025-09-14T10:27:09.022424",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>view_to_cart_rate</th>\n",
       "      <th>cart_to_purchase_rate</th>\n",
       "      <th>view_to_purchase_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.8246</td>\n",
       "      <td>0.3494</td>\n",
       "      <td>0.2881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.5691</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.9232</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.0024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>0.0043</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.6871</td>\n",
       "      <td>0.5547</td>\n",
       "      <td>0.3811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster  view_to_cart_rate  cart_to_purchase_rate  view_to_purchase_rate\n",
       "0        0             0.8246                 0.3494                 0.2881\n",
       "1        1             0.0265                 0.0052                 0.0001\n",
       "2        2             1.5691                 0.0036                 0.0056\n",
       "3        3             0.9232                 0.0026                 0.0024\n",
       "4        4             0.0265                 0.0043                 0.0001\n",
       "5        5             0.6871                 0.5547                 0.3811"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 总转化率表 / Cluster-level Overall Conversion Table\n",
    "# 1) 汇总所有月份的 stage 数量 / Aggregate stage counts over all months\n",
    "overall_stage = (\n",
    "    stage_counts.groupby('cluster')\n",
    "    .agg(\n",
    "        view_cnt     = ('view_cnt',     'sum'),\n",
    "        cart_cnt     = ('cart_cnt',     'sum'),\n",
    "        purchase_cnt = ('purchase_cnt', 'sum')\n",
    "    )\n",
    ")\n",
    "\n",
    "# 2) 计算三类总转化率 / Compute overall rates (handle divide-by-zero)\n",
    "overall_stage['view_to_cart_rate']     = np.where(\n",
    "    overall_stage['view_cnt'] > 0,\n",
    "    overall_stage['cart_cnt'] / overall_stage['view_cnt'],\n",
    "    0.0\n",
    ")\n",
    "overall_stage['cart_to_purchase_rate'] = np.where(\n",
    "    overall_stage['cart_cnt'] > 0,\n",
    "    overall_stage['purchase_cnt'] / overall_stage['cart_cnt'],\n",
    "    0.0\n",
    ")\n",
    "overall_stage['view_to_purchase_rate'] = np.where(\n",
    "    overall_stage['view_cnt'] > 0,\n",
    "    overall_stage['purchase_cnt'] / overall_stage['view_cnt'],\n",
    "    0.0\n",
    ")\n",
    "\n",
    "# 3) 选取并格式化输出列 / Select & format for display\n",
    "summary_cols = [\n",
    "    'view_to_cart_rate',\n",
    "    'cart_to_purchase_rate',\n",
    "    'view_to_purchase_rate'\n",
    "]\n",
    "overall_conv_table = (\n",
    "    overall_stage[summary_cols]\n",
    "    .round(4)\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# 4) 显示 DataFrame / Display the table\n",
    "overall_conv_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc4cb136",
   "metadata": {
    "papermill": {
     "duration": 0.017909,
     "end_time": "2025-09-14T10:27:09.104331",
     "exception": false,
     "start_time": "2025-09-14T10:27:09.086422",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3.4 用户画像 / User Profiling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "647180ef",
   "metadata": {
    "papermill": {
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     "start_time": "2025-09-14T10:27:09.122691",
     "status": "completed"
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    "tags": []
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   "source": [
    "根据 [\\[2.2.4 簇特征概览\\]](#2.2-K-means-聚类-/-K-means-Clustering)，[\\[总转化率表\\]](#3.3-转化率漏斗-/-Conversion-Funnel)，以及[\\[留存热力图\\]](#3.2-留存热力图-/-Retention-Heatmap-by-Segment)，我们可以创建用户画像并进行对应的精细化运营。\n",
    "> **注意**：留存热力图右下部分因时间未到而显示 0%；转化率 & 留存图包含了测试期用户预测的簇标签，可能与训练期簇特征有偏差。\n",
    "\n",
    "---\n",
    "\n",
    "Based on [\\[2.2.4 Cluster Feature Overview\\]](#2.2-K-means-Clustering), [\\[Cluster-level Overall Conversion Table\\]](#3.3-Conversion-Funnel), and [\\[Retention Heatmap by Segment\\]](#3.2-Retention-Heatmap-by-Segment), we can create user profiles and perform corresponding refined operations.\n",
    "\n",
    "> **Note**: The bottom-right portion of the retention heatmap shows 0% because the corresponding time period has not yet occurred. Both the conversion and retention charts include predicted cluster labels for users in the test period, which may differ from the cluster features in the training period."
   ]
  },
  {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 簇 0 / Cluster 0"
   ]
  },
  {
   "cell_type": "markdown",
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     "status": "completed"
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   },
   "source": [
    "**Cluster 0 用户画像**\n",
    "\n",
    "* **核心价值群体**：占比仅 3% (42,326 人)，但拥有最高的用户消费力（中位 `frequency` ≈ 23, `monetary` ≈ 103）→ 平台的收入支柱。\n",
    "* **高效决策者**：拥有极高的转化效率，`View→Cart ≈ 82.5%`，`Cart→Purchase ≈ 34.9%` → 购物目的性极强，是成熟的消费者。\n",
    "* **近期流失风险**：中位 `recency` ≈ 60 天，是其最关键的风险特征 → 这批高价值用户正处于“休眠”状态。\n",
    "* **历史忠诚度高**：留存分析显示其历史粘性非常强，次月复购率可达 20%-37% → 证明其对平台或品牌有深度认可。\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 0 精细化运营建议**\n",
    "\n",
    "1.  **VIP专属召回计划**\n",
    "    通过邮件(EDM)/短信(SMS)等渠道，推送“VIP专属回归礼券”或高价值无门槛优惠券，强调其尊贵身份，用高价值感召回用户。\n",
    "\n",
    "2.  **个性化补货提醒**\n",
    "    基于用户历史购买记录，智能计算其消耗周期，并发送“您常用的XXX快用完了吗？”之类的补货提醒，附上专属优惠，精准唤醒需求。\n",
    "\n",
    "3.  **用户关怀与流失调研**\n",
    "    发送“我们想念您”主题的关怀邮件，并附上一个简短的调研问卷（可用积分或小额优惠券作为激励），了解用户近期的流失原因，为后续策略优化提供依据。\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 0 User Profiling**\n",
    "\n",
    "* **Core Value Segment**: Comprises only 3% of users (42,326) but boasts the highest purchasing power (median `frequency` ≈ 23, `monetary` ≈ 103) → The platform's revenue backbone.\n",
    "* **Efficient Decision-Makers**: Exhibits outstanding conversion efficiency, with `View→Cart ≈ 82.5%` and `Cart→Purchase ≈ 34.9%` → Highly intentional and mature shoppers.\n",
    "* **Recent Churn Risk**: Median `recency` of ≈ 60 days is their most critical risk factor → This high-value group is currently \"hibernating.\"\n",
    "* **Historically Loyal**: Retention analysis shows strong historical stickiness, with next-month repurchase rates ranging from 20% to 37% → Indicates a deep-seated trust in the platform or brand.\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 0 Targeted Strategies**\n",
    "\n",
    "1.  **VIP-Exclusive Win-Back Program**\n",
    "    Use channels like EDM/SMS to send \"VIP Welcome Back\" offers or high-value, no-threshold coupons, emphasizing their exclusive status to entice them back with a sense of high value.\n",
    "\n",
    "2.  **Personalized Restock Reminders**\n",
    "    Based on their purchase history, intelligently calculate their product consumption cycle and send reminders like, \"Running low on your favorite XXX?\" along with an exclusive discount to precisely reactivate their needs.\n",
    "\n",
    "3.  **Customer Care & Churn Survey**\n",
    "    Send a \"We Miss You\" themed email that includes a short survey (incentivized with points or a small coupon) to understand the reasons for their recent inactivity, providing valuable data for future strategic adjustments."
   ]
  },
  {
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     "status": "completed"
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   },
   "source": [
    "### 簇 1 / Cluster 1"
   ]
  },
  {
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     "status": "completed"
    },
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   },
   "source": [
    "**Cluster 1 用户画像**\n",
    "\n",
    "* **绝对主力流量池**：占比高达 68% (1,112,742 人)，是构成平台用户基数的“路人”群体。\n",
    "* **“一次性”浏览访客**：核心行为是“看一眼就走”，中位 `view_cnt` ≈ 1.4，而加购、购买等后续行为几乎为零。\n",
    "* **几乎无转化意愿**：转化率极低，`View→Purchase` 仅有 0.01% → 他们是机会型访客，缺乏明确的购物需求。\n",
    "* **忠诚度无法评估**：绝大多数用户无购买记录。极少数的购买者均集中在最后一个月，无法追踪其后续复购行为，因此整个群体可被视为无忠诚度。\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 1 精细化运营建议**\n",
    "\n",
    "1.  **优化落地页与新客引导**\n",
    "    针对这部分流量巨大但互动极低的用户，首要任务是优化他们首次触达的页面（Landing Page）。确保页面加载速度、突出卖点（如“全网销量第一”）、增加用户评价等社会认同信息，并设置清晰的新手引导或优惠券弹窗。\n",
    "\n",
    "2.  **设置低门槛转化抓手**\n",
    "    既然直接转化购买很难，目标应转为“留资”。通过设置“订阅邮件享9折优惠”、“参与肌肤测试领取小样”等低门槛互动，获取用户的联系方式（如邮箱），将其转化为可以进行后续营销的线索。\n",
    "\n",
    "3.  **部署再营销策略**\n",
    "    为网站布设再营销代码（如Meta Pixel, Google Ads Tag）。当这些用户访问后离开，可以在社交媒体或谷歌广告网络中，以较低的成本向他们重新展示他们浏览过的商品或相关的畅销品，以期在未来某个时点重新唤醒他们的兴趣。\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 1 User Profiling**\n",
    "\n",
    "* **Absolute Main Traffic Pool**: Constitutes a massive 68% of users (1,112,742), representing the platform's broad \"passer-by\" base.\n",
    "* **\"One-Time\" Browsing Visitors**: Their core behavior is \"view and leave,\" with a median `view_cnt` of ≈ 1.4, while subsequent actions like adding to cart or purchasing are virtually zero.\n",
    "* **Virtually No Conversion Intent**: Conversion rates are extremely low, with a `View→Purchase` rate of a mere 0.01% → They are opportunistic visitors lacking clear shopping goals.\n",
    "* **Indeterminable Loyalty**: The vast majority have no purchase history. The tiny fraction of purchasers all appeared in the final month of the dataset, making it impossible to track repeat behavior. Thus, the segment as a whole is considered to have no loyalty.\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 1 Targeted Strategies**\n",
    "\n",
    "1.  **Optimize Landing Page & New User Guidance**\n",
    "    For this high-volume, low-engagement traffic, the priority is to optimize their first point of contact (the Landing Page). Ensure fast loading speeds, highlight key selling points (e.g., \"No. 1 Best-Seller\"), incorporate social proof like user reviews, and implement clear new-user guides or coupon pop-ups.\n",
    "\n",
    "2.  **Implement Low-Barrier Conversion Hooks**\n",
    "    Since direct conversion to purchase is difficult, the goal should shift to lead generation. Use low-barrier interactions like \"Subscribe for 10% Off\" or \"Take the Skincare Quiz to Get a Free Sample\" to capture user contact information (like emails), turning them into leads for future marketing.\n",
    "\n",
    "3.  **Deploy Retargeting Strategies**\n",
    "    Install retargeting pixels (e.g., Meta Pixel, Google Ads Tag) on the site. After these users visit and leave, you can re-engage them at a low cost on social media or the Google Display Network by showing ads for products they viewed or related best-sellers, aiming to re-spark their interest at a later time. "
   ]
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   "source": [
    "### 簇 2 / Cluster 2"
   ]
  },
  {
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     "status": "completed"
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   "source": [
    "**Cluster 2 用户画像**\n",
    "\n",
    "* **显著的“加购狂”**：占比 10% (160,553 人)，最怪异的特征是 `View→Cart` 转化率高达 157%，中位 `cart_cnt` (4.87) 显著高于 `view_cnt` (3.10) → 这表明他们频繁使用“快速加购”功能，或将同一商品多次加入购物车，可能是在用购物车进行比较或收藏。\n",
    "* **极低的购买转化**：`Cart→Purchase` 转化率仅为 0.36%，几乎可以忽略不计 → 他们是典型的“加购即放弃”用户，购物车是他们行为的终点，而非起点。\n",
    "* **“一次性”高兴趣访客**：用户行为非常近期 (`recency` ≈ 0)，但留存图显示后续月份几乎无任何复购 → 他们可能是被特定活动或商品吸引而来的冲动型访客，但兴趣消散极快。\n",
    "* **高度不稳定性**：留存热力图完全不符合商业逻辑，这可能意味着该簇是算法产生的一个“边缘”或“混合”群体，其共性不如其他簇明显。\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 2 精细化运营建议**\n",
    "\n",
    "1.  **引入“加入收藏”功能**\n",
    "    这部分用户可能将购物车当成了“收藏夹”。在加购按钮旁增加一个明显的“加入收藏”或“Save for Later”按钮，将“高意向购买”和“暂时收藏”的行为分流。这既能优化用户体验，也能让营销团队获得更纯净的购物车数据。\n",
    "\n",
    "2.  **优化购物车体验与费用透明化**\n",
    "    用户在加购后迅速流失，可能是被未预见的费用（如运费、税费）“劝退”。在购物车页面或侧边栏**提前估算并展示总费用**，可以有效管理用户预期，减少进入结算流程后的放弃率。\n",
    "\n",
    "3.  **部署智能“废弃购物车”挽回策略**\n",
    "    针对这批海量“废弃购物车”的用户，建立自动化的挽回流程是关键。在用户加购后1-2小时，通过邮件或App Push发送提醒，内容可分三步：\n",
    "    * **步骤一 (提醒)**：“您购物车里的宝贝还在等您哦！”\n",
    "    * **步骤二 (制造稀缺感)**：“您心仪的商品库存不多了，快去看看吧！”\n",
    "    * **步骤三 (轻度激励)**：“完成您的订单，即可享受专属小折扣。”\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 2 User Profiling**\n",
    "\n",
    "* **The \"Aggressive Carters\"**: Comprising 10% of users (160,553), their most bizarre trait is a `View→Cart` conversion rate of 157%, with a median `cart_cnt` (4.87) notably higher than `view_cnt` (3.10) → This suggests frequent use of \"quick add\" features or adding the same item multiple times, possibly using the cart for comparison or as a wishlist.\n",
    "* **Extremely Low Purchase Conversion**: The `Cart→Purchase` conversion rate is a negligible 0.36% → They are classic \"add-and-abandon\" users, for whom the shopping cart is the end of their journey, not the beginning.\n",
    "* **\"One-Off\" High-Interest Visitors**: User activity is very recent (`recency` ≈ 0), but the retention chart shows almost no subsequent repurchasing → They are likely impulsive visitors drawn in by a specific event or product, but their interest fades extremely quickly.\n",
    "* **Highly Unstable**: The retention heatmap defies business logic, which may imply this cluster is an \"edge\" or \"mixed\" group generated by the algorithm, with less distinct commonalities than other clusters.\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 2 Targeted Strategies**\n",
    "\n",
    "1.  **Introduce a \"Save to Wishlist\" Feature**\n",
    "    This segment may be using the shopping cart as a \"wishlist.\" Add a prominent \"Add to Wishlist\" or \"Save for Later\" button next to the cart button to separate \"high-intent\" from \"saving-for-later\" behaviors. This improves user experience and provides the marketing team with cleaner cart data.\n",
    "\n",
    "2.  **Optimize Cart Experience & Fee Transparency**\n",
    "    The rapid drop-off after adding to cart could be due to unexpected costs (like shipping or taxes). **Proactively estimating and displaying the total cost** in the cart page or sidebar can effectively manage user expectations and reduce abandonment rates during the checkout process.\n",
    "\n",
    "3.  **Deploy Smart Cart Abandonment Campaigns**\n",
    "    For this massive volume of abandoned carts, an automated recovery flow is crucial. Trigger an email or push notification 1-2 hours after abandonment, following a three-step sequence:\n",
    "    * **Step 1 (Reminder)**: \"The items in your cart are waiting for you!\"\n",
    "    * **Step 2 (Create Scarcity)**: \"Your favorite items are running low on stock, check them out now!\"\n",
    "    * **Step 3 (Light Incentive)**: \"Complete your order to enjoy a small exclusive discount.\""
   ]
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   "source": [
    "### 簇 3 / Cluster 3"
   ]
  },
  {
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    "**Cluster 3 用户画像**\n",
    "\n",
    "* **高参与度的“研究员”**：占比 3% (52,816 人)，其最显著的特征是极高的浏览和购物车互动。中位 `view_cnt` ≈ 29，`cart_cnt` ≈ 27，`remove_cnt` ≈ 18 → 他们在网站上投入了大量时间，深度研究和挑选商品。\n",
    "* **“加购-购买”断层严重**：`View→Cart` 转化率高达 92.3%，证明他们对商品有浓厚兴趣。然而，`Cart→Purchase` 转化率骤降至 0.26% → 临门一脚的转化是这个群体最大的痛点。\n",
    "* **犹豫不决的行为特征**：高达 18 次的“移除购物车”中位次数是该群体的独特标签。→ 这表明他们反复权衡、对比，可能在价格、选择、或外部因素上极度犹豫。\n",
    "* **缺乏忠诚度**：用户行为非常近期 (`recency` ≈ 0)，结合不稳定的留存数据来看，可以判断他们的深度逛览行为是单次的，并未转化为持续的复购习惯。\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 3 精细化运营建议**\n",
    "\n",
    "1.  **推出“限时购物车”专属折扣**\n",
    "    针对他们的犹豫不决，用“时间”作为催化剂。当系统检测到用户购物车内商品停留超过24小时，可自动触发一封邮件或App Push，提供一个“24小时内有效的9折优惠码”，通过制造稀缺感和紧迫感，促使其下定决心。\n",
    "\n",
    "2.  **引入“对比清单”与“降价提醒”功能**\n",
    "    既然他们是“研究员”，就为他们提供研究工具。允许用户将购物车内的商品一键加入“对比清单”，直观比较参数和价格。同时，在购物车内为商品增加“降价通知我”的按钮，将他们犹豫的行为转化为一次有效的用户授权和未来的营销触点。\n",
    "\n",
    "3.  **强化社会认同与推荐**\n",
    "    为了帮助他们克服“选择困难症”，在购物车页面对商品进行标签化引导。例如，为商品打上“热销爆款”、“95%用户好评”、“本周回购榜TOP1”等标签，利用他人的选择来给他们提供购买信心，降低决策成本。\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 3 User Profiling**\n",
    "\n",
    "* **Highly-Engaged \"Researchers\"**: This 3% segment (52,816 users) is defined by extremely high browsing and cart interaction. Median `view_cnt` ≈ 29, `cart_cnt` ≈ 27, and `remove_cnt` ≈ 18 → They invest significant time on the site, deeply researching and curating products.\n",
    "* **Severe Cart-to-Purchase Gap**: Their `View→Cart` conversion rate is an impressive 92.3%, indicating strong product interest. However, the `Cart→Purchase` rate plummets to just 0.26% → The final step of conversion is this group's biggest pain point.\n",
    "* **Indecisive Behavior Pattern**: A median `remove_from_cart` count of ≈18 is this group's unique signature. → This shows they are repeatedly weighing options and comparing items, likely hesitating on price, choice, or other external factors.\n",
    "* **Lack of Loyalty**: User activity is very recent (`recency` ≈ 0), and combined with the unstable retention data, it's clear their deep browsing is a one-off event that does not translate into a consistent repurchase habit.\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 3 Targeted Strategies**\n",
    "\n",
    "1.  **Offer Time-Limited Cart Discounts**\n",
    "    Counter their indecisiveness by using time as a catalyst. When the system detects items have been in a cart for over 24 hours, automatically trigger an email or push notification with a \"10% off coupon, valid for 24 hours only.\" This creates scarcity and urgency to push them toward a decision.\n",
    "\n",
    "2.  **Introduce Comparison & Price Drop Alert Features**\n",
    "    If they are \"researchers,\" give them research tools. Allow users to add cart items to a \"Comparison List\" for a side-by-side view of specs and prices. Additionally, add a \"Notify me of price drops\" button for cart items, converting their hesitation into a valuable user permission and a future marketing touchpoint.\n",
    "\n",
    "3.  **Leverage Social Proof & Recommendations**\n",
    "    To help them overcome \"choice paralysis,\" use guiding labels within the shopping cart. Tag products with labels like \"Best-Seller,\" \"95% Positive Reviews,\" or \"Top Repurchased Item of the Week.\" This use of social proof can build their confidence and lower their decision-making friction."
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   "source": [
    "### 簇 4 / Cluster 4"
   ]
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    "**Cluster 4 用户画像**\n",
    "\n",
    "* **有一定兴趣的浏览者**：占比 12% (204,606 人)，他们的核心特征是进行了中等程度的浏览（中位 `view_cnt` ≈ 9），但几乎没有其他互动 → 他们比“路人”用户更有好奇心，但兴趣尚未转化为行动。\n",
    "* **“只看不加”的用户**：`View→Cart` 转化率仅 2.65%，后续购买转化率几乎为零 → 他们的浏览行为是发散的，缺乏明确的加购意向。\n",
    "* **缺乏购买记录与忠诚度**：中位数购买数据为零，且留存图数据不可靠 → 表明这是一个由无购买行为的新访客或非活跃用户组成的群体，没有忠诚度可言。\n",
    "* **数据不稳定性提醒**：该簇的留存热力图不符合正常模式，因此对该群体长期行为的任何判断都应非常谨慎。\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 4 精细化运营建议**\n",
    "\n",
    "1.  **通过内容营销深化兴趣**\n",
    "    既然用户愿意浏览近10个页面，说明他们对品类有兴趣。可以在商品详情页中嵌入“成分科普”、“使用教程”的博客文章或视频链接，或者提供“如何选择适合您的精华”之类的购买指南，将他们从单纯的“看商品”引导至“学知识”，从而建立品牌信任感。\n",
    "\n",
    "2.  **利用互动工具主动引导**\n",
    "    对于有浏览行为但无下一步动作的用户，可以用互动来破冰。例如，在网站设置“智能测肤”、“匹配你的专属色号”等趣味性测试(Quiz)，用户完成测试后直接推荐最适合的1-2款商品。这种方式比让用户自己在9个页面中选择，转化路径更短。\n",
    "\n",
    "3.  **激励用户订阅邮件列表**\n",
    "    这类用户是潜在的营销线索。可以设置当用户浏览超过5个页面后，触发一个“订阅我们的资讯，获取专业护肤技巧及首次购物9折优惠”的弹窗，将这些匿名的浏览者转化为可以长期沟通的邮件列表订阅者。\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 4 User Profiling**\n",
    "\n",
    "* **Browsers with Latent Interest**: A 12% segment (204,606 users), their core characteristic is moderate browsing (median `view_cnt` ≈ 9) with almost no other interaction → They are more curious than \"passer-by\" users, but this interest has not yet converted into action.\n",
    "* **The \"Look, Don't Add\" User**: The `View→Cart` conversion rate is only 2.65%, with subsequent purchase conversions being near-zero → Their browsing is exploratory and lacks clear intent to add items to the cart.\n",
    "* **Lacking Purchase History & Loyalty**: Median purchase data is zero, and the retention chart is unreliable → This indicates a group of non-purchasing new visitors or inactive users with no established loyalty.\n",
    "* **Data Instability Note**: This cluster's retention heatmap does not follow a normal pattern, so any judgment about its long-term behavior should be made with extreme caution.\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 4 Targeted Strategies**\n",
    "\n",
    "1.  **Deepen Interest with Content Marketing**\n",
    "    Since users are willing to view nearly 10 pages, they have an interest in the category. Embed links within product pages to \"Ingredient Explained\" blog posts, video tutorials, or buying guides like \"How to Choose the Right Serum for You.\" This guides them from simply \"viewing products\" to \"learning,\" which helps build brand trust.\n",
    "\n",
    "2.  **Use Interactive Tools for Proactive Guidance**\n",
    "    For users who browse but don't take the next step, use interaction to break the ice. For example, implement fun quizzes on the site like \"Smart Skin Analysis\" or \"Find Your Perfect Shade.\" After completion, recommend 1-2 highly suitable products directly. This provides a much shorter conversion path than letting them choose from 9 different pages.\n",
    "\n",
    "3.  **Incentivize Newsletter Subscription**\n",
    "    These users are potential marketing leads. Configure a pop-up to trigger after a user has viewed more than 5 pages, offering \"Subscribe to our newsletter for pro skincare tips and 10% off your first order.\" This converts anonymous browsers into long-term, communicable leads. "
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   "source": [
    "### 簇 5 / Cluster 5"
   ]
  },
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    "**Cluster 5 用户画像**\n",
    "\n",
    "* **高效转化的潜力股**：占比 4% (66,315 人)，拥有平台中最高的转化率：`Cart→Purchase` 高达 55.5%，`View→Purchase` 达到 38.1% → 一旦他们决定购物，决策就极为果断，几乎没有犹豫。\n",
    "* **中度价值贡献者**：具备一定的重复购买历史（中位 `frequency` ≈ 4.5），消费金额适中 → 他们是平台的的价值客户，而非“一锤子买卖”。\n",
    "* **严重流失状态**：最危险的信号是其高达 78 天的 `recency` 中位数 → 这意味着这批高效的消费者平均已超过两个半月没有回归。\n",
    "* **忠诚度偏低**：留存热力图符合逻辑，但也显示出他们的次月留存率仅在 2-3% 左右徘徊 → 他们很容易流失，并未在平台形成稳固的购物习惯。\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 5 精细化运营建议**\n",
    "\n",
    "1.  **实施“直达主题”式召回**\n",
    "    对于这群决策果断的用户，营销信息应避免花哨，直击要点。通过邮件/短信发送简单直接、吸引力强的优惠，例如“【限时回归】全场免运费”或“您有一张20% OFF优惠券待查收”，最大程度降低他们的决策成本，促使其“高效”回归。\n",
    "\n",
    "2.  **试行“补货提醒”与“定期购”**\n",
    "    他们可能是需求驱动型消费者（用完了才买）。可以基于其历史订单（如购买了消耗品），在预估的消耗周期末（如第60-75天）发送“您常用的XXX需要补货了吗？”的提醒。同时，对他们购买过的消耗品推出“定期购享折扣”功能，将他们单次的“高效转化”变为持续的“忠诚锁定”。\n",
    "\n",
    "3.  **发起流失原因调研**\n",
    "    这群用户在站内的转化毫无障碍，但就是不再回来，原因很可能出在“购买后”的体验（如物流、商品、售后）。可以针对性地发送一份简短的调研问卷，附上小额奖励，询问他们上次购物体验如何，以及不再光顾的原因，从而发现运营或服务中的盲点。\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 5 User Profiling**\n",
    "\n",
    "* **High-Efficiency Potential Customers**: A 4% segment (66,315 users) boasting the platform's highest conversion rates: `Cart→Purchase` is an outstanding 55.5%, and `View→Purchase` is 38.1% → Once they decide to buy, they are extremely decisive and rarely hesitate.\n",
    "* **Moderate Value Contributors**: They have a history of repeat purchases (median `frequency` ≈ 4.5) with moderate spending → They are valuable, established customers, not just one-time buyers.\n",
    "* **Seriously Lapsed State**: Their most critical risk signal is a high median `recency` of ≈ 78 days → This means these efficient shoppers have been inactive for over 2.5 months on average.\n",
    "* **Low Loyalty**: The cohort retention chart, while logical, shows that their next-month retention hovers at a low 2-3% → They churn easily and have not formed a stable shopping habit on the platform.\n",
    "\n",
    "---\n",
    "\n",
    "**Cluster 5 Targeted Strategies**\n",
    "\n",
    "1.  **Implement a \"To-the-Point\" Win-Back Strategy**\n",
    "    For this decisive group, marketing messages should be direct and skip the fluff. Send simple, strong offers via email/SMS, such as \"[Limited-Time Return Offer] Free Shipping on All Orders\" or \"You Have a 20% OFF Coupon Waiting.\" This minimizes their decision-making friction and encourages an \"efficient\" return.\n",
    "\n",
    "2.  **Pilot \"Restock Reminders\" & \"Subscribe & Save\"**\n",
    "    They may be need-based consumers who only buy when they run out of a product. Based on their order history (especially for consumables), send a \"Time to restock your XXX?\" reminder around the estimated end of the product's lifecycle (e.g., day 60-75). Additionally, introduce a \"Subscribe & Save\" feature for these items to convert their one-off efficient purchases into locked-in loyalty.\n",
    "\n",
    "3.  **Launch a Churn Reason Survey**\n",
    "    These users convert seamlessly on-site but don't return, suggesting the problem may lie in the post-purchase experience (e.g., shipping, product quality, customer service). Target them with a short survey, incentivized with a small reward, asking about their last experience and reasons for not returning. This can help uncover blind spots in operations or service."
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